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ChatGPT and Deception

photograph of exams stacked on desk at front of class

For many, writing can be a challenging, even painful, experience. What seems like it should be a simple job – taking the thoughts in your head, turning them into words, and then typing them out – can be a task of almost insurmountable odds for some. Heck, I do it for a living, and I still find the blank page to be an ungodly sight, one that I sometimes flee from and promise to tackle later on.

As such, it should be no surprise that the advent of an AI system that can write for us has caused quite a stir. That system is ChatGPT, and while I could explain what it is, it’s more effective (not to mention easier) to let the tool do that for itself:

ChatGPT is a state-of-the-art language generation model developed by OpenAI. It is based on a transformer architecture and pre-trained on a large dataset of text, allowing it to generate human-like text.

ChatGPT can be fine-tuned for various natural language processing tasks such as language translation, text summarization, question answering, and text completion. It can also be used to generate creative writing, poetry, and even code.

One of the key features of ChatGPT is its ability to generate text that is coherent and coherent with the context it was given, making it able to generate text that flows well and makes sense. ChatGPT can be used to generate text on a wide range of topics and can be fine-tuned to suit specific use cases.

It is important to note that ChatGPT is a machine learning model, it is not capable of independent thinking or decision making, it is only able to generate text based on patterns it has learned from the data it was trained on.

I didn’t write any of that. Instead, I gave ChatGPT the prompt “explain what ChatGPT is” and let it fly. As it just noted, however, ChatGPT is not limited to dry, explanatory statements. The system has demonstrated an apparent aptitude for creative, even funny, writing, from explaining quantum theory to a child in the style of Snoop Dogg to creating an analogy for what it’s like to eat the keto diet, from giving an account of Attack on Titan in the manner of Donald Trump to writing a biblical verse explaining how to remove a peanut butter sandwich from a VCR. The tool really does seem adaptable.

Yet, despite the hilarity, ChatGPT’s emergence has brought some pressing issues regarding ownership and authenticity of work to the fore. If an AI generates text for you, can you claim it as your own? For example, Ammaar Reshi is facing considerable backlash for using ChatGPT to write a children’s book (which he then illustrated using Midjorney, an AI art generator). Reshi did not directly write or illustrate the book he is claiming as his product; he gave ChatGPT the required prompts and then used its output.

But, it has been in the educational sector where such concerns have really taken hold. So much so that some, such as New York City’s Department of Education, have blocked access to ChatGPT on school devices for fear of its misuse. The problems are relatively easy to grasp:

What is stopping students from passing off ChatPGT-produced essays and other forms of assessed work as their own? How should educators respond if a student uses ChatGPT to write an essay? And are students actually doing anything wrong if they use ChatGPT like this?

The answer to this last question is vastly complex and intertwined with the very purpose of assessment and learning monitoring. The point of assigning assessments, such as essays, is not so students produce a piece of text. The production of the essay is merely a step towards another goal. These forms of assessment act as a representation of the students’ learning. When a teacher asks you to write a 3,000-word paper on Frederick Douglas, for example, it is not the paper with which they are concerned; it is with your ability to recall, appraise, and communicate what you know about Douglas’ life, work, and impact. The essay is a medium through which such appraisal is conducted.

As philosopher Rebecca Mace remarked in an episode of BBC’s Inside Science:

A lot of people, including the newspapers, seem to have misunderstood the point of homework. So the purpose of homework is not to produce an essay, but to assess student understanding in order that the teachers can assist them with the gaps, or work out what they’ve not taught very well, or what they maybe need to go over again, or what that individual student really needs help with. Then the essay itself is irrelevant in many ways because that’s all the essay’s doings; it’s a means to an end.

Thus, according to such a way of thinking, the danger of ChatGPT comes from its potential to misrepresent student learning, giving the impression that a student knows more about a subject than they actually do. The issue is not one of principle but of outcome, and the use of ChatGPT brings with it the risk that learning is negatively impacted.

This stance, however, seems to overlook something important in using ChatGPT in educational settings. If accurate – if the threat of ChatGPT comes from its capacity to hide academic failings (both on the student’s and teacher’s behalf) – then we shouldn’t have any qualms about it being used in situations where this isn’t a factor. But, academically gifted students who know their subjects inside and out still seem to commit some wrong when they pass the algorithmically-generated text off as their own. This wrong emerges not from the impact such usage might have on their academic performance, nor on their teacher’s ability to assess their grasp of a subject accurately, but from the fact that they are attempting to deceive their assessor. It is wrong not because of an outcome but because of an adherence to principles – the virtue of honesty and the vice of deception.

That is not to say that this is the only reason why ChatGPT presents a potential harm to education and educational practices. The use of AI to game the academic-assessment system by hiding one’s failure to meet the standards are most certainly a concern (perhaps the central one). But, such an acknowledgement should not lead us to overlook the fact that, much like plagiarism, academic wrongs don’t simply emerge from their deleterious impact. They also come from deception and attempting to pass something off as one’s work when, in fact, they had minimal input in its creation.

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Man v. Machine: The Ethics of AI Art

The creation of digital art is nothing new, but advances in artificial intelligence have created a novel environment where all sorts of media can now be created without much human input. When Jason M. Allen won the Colorado State Fair for his piece “Théâtre d’Opéra Spatial” it sparked fierce debate about the nature of art and the merits of AI creations. But we should be careful not to confuse the many ethical issues involved. Is the outcry about the fairness of contests or about the future of art?

Allen, a video game designer, created his entry using an AI called Midjourney. It works by creating images from textual descriptions. Allen claims that he created hundreds of images before selecting only three. He then made some additional adjustments using Photoshop and boosted their resolution with a tool called Gigapixel. He entered the Colorado State Fair under the digital arts category which is defined as “artistic practice that uses digital technology as part of the creative or presentation process.” Allen claims that he informed the competition that the image was created with AI. The competition’s judges, meanwhile, say that they were not aware. Nevertheless, they said they would still have given it first place based on the piece itself.

Online reaction was harsh.

While the uproar isn’t surprising, it’s not clear that everyone has the same objections for the same reasons.

Let’s address the first major ethical question which centers around the contest: Was it wrong of Allen to submit the AI created work and be awarded the blue ribbon over other artists who created their works by hand? The contest’s definition of digital arts was sufficiently broad enough that AI created works were eligible. The work was entered using the name “Jason M. Allen via Midjourney.” Also, according to Allen, this was not simply a case of a few button presses, but 80 hours of work – tweaking the prompts to get the image he wanted and making a selection out of 900 iterations. While Allen spent his time differently than the other artists, this doesn’t mean that creating the image lacked skill, effort, or the aesthetic taste.

On the other hand, others might object that it was wrong for Allen to enter the contest since he was not the artist; it was the artificial intelligence who actually created the piece of art. Did the AI create the work, or is the AI merely a tool for Allen – the true creator – to manipulate?

The judges selected this particular work because of the impact it had on them, and Allen was deliberately attempting to tie together the themes that the painting conveys. The AI, meanwhile, has no notion of the impact that imagery might have; it doesn’t think any differently about the art conveyed by painting 899 or 900.

To further complicate things, the AI’s creation is based on training data from other artists, raising the issue of plagiarism. While the AI piece is not a direct copy, it does take “inspiration” from the art it was trained with. Often art is about meshing together styles and techniques to create something new, so it is difficult to view this purely as copying other artists. If the piece is not a copy of other artists, and if the AI is not the artist, then it stands to reason that Allen is the artist. If not, then this would be a piece of art without an artist, to which many might say that it therefore is not a piece of art at all and thus should not be allowed entry in the contest.

Answering the question “what is art?” might tell us if AI can actually create such a thing, but such a definition is hard to pin down and it’s easy to rely on preconceived notions. Generally, art follows certain aesthetic patterns and conveys content that people find meaningful. The judges awarded the prize based on the spirit it invoked. We can’t unpack such a complicated question here, but we should ask whether this understanding of the creative process truly threatens art. Was Allen right to declare that “Art is dead Dude”? Is there any role left for artists?

When the camera was first developed, people said that it would mean the end of the painter, but obviously painters still exist today. Ultimately, the human artist brings a kind of authenticity and uniqueness to the work.

AI doesn’t replace aesthetic choice and esthetic judgment since at the end of the day, it is we who must decide if anything produced has merit. While the role of the artist may be changing, their place in whatever system that produces such works remains paramount.

A final ethical issue is the question of the future of the artist in general. Even if we accept that Allen did nothing wrong, many still decry the end of the professional artist. As digital artist RJ Palmer claims, “This thing wants our jobs, it’s actively anti-artist.” Even if we accept that Allen’s work itself isn’t plagiarism, there is no denying that AI produced images only work by being trained on the work of real artists, which the algorithm can then borrow any stylistic elements it wants. This has the potential to create an intellectual property nightmare since smaller artist won’t be able to profit from their work to nearly the same degree as a company using AI, which will produce images in the style of that artist at a far faster pace. Federal courts are now hearing a case over whether the U.S. Copyright Office was wrong to reject a copyright for an AI-made piece.

Of course the application of AI to a given field and the threat that it creates to the workforce is not confined to the world of art. Eventually there may be legal and industry reform that can mitigate some of these issues, but many artists will no doubt suffer and it could undercut the art industry as whole. As one artist notes, it isn’t so much that AI can create something, but that it will always be a kind of “derivative, generated goo.” Clearly, the implications of Allen’s win run deeper than a single blue ribbon.

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AI Writing and Epistemic Dilution

There is a lot of debate surrounding the ethics of artificial intelligence (AI) writing software. Some people believe that using AI to write articles or create content is unethical because it takes away opportunities from human writers. Others believe that AI writing software can be used ethically as long as the content is disclosed as being written by an AI. At the end of the day, there is no easy answer to whether or not we should be using AI writing software. It depends on your personal ethical beliefs and values.

That paragraph wasn’t particularly compelling, and you probably didn’t learn much from reading it. That’s because it was written by an AI program: in this case, I used a site called Copymatic, although there are many other to choose from. Here’s how Copymatic describes its services:

Use AI to boost your traffic and save hours of work. Automatically write unique, engaging and high-quality copy or content: from long-form blog posts or landing pages to digital ads in seconds.

Through some clever programming, the website takes in prompts on the topic you want to write about (for this article, I started with “the ethics of AI writing software”), scours the web for pieces of information that match those prompts, and patches them together in a coherent way. It can’t produce new ideas, and, in general, the more work it has to do the less coherent the text becomes. But if you’re looking for content that sounds like a book report written by someone who only read the back cover, these kinds of programs could be for you.

AI writing services have received a lot of attention for their potential to automate something that has, thus far, eluded the grasp of computers: stringing words together in a way that is meaningful. And while the first paragraph is unlikely to win any awards for writing, we can imagine cases in which an automated process to produce writing like this could be useful, and we can easily imagine these programs getting better.

The AI program has identified an ethical issue, namely taking away jobs from human writers. But I don’t need a computer to do ethics for me. So instead, I’ll focus on a different negative consequence of AI writing, what I’ll call epistemic dilution.

Here’s the problem: there are a ridiculous number of a certain type of article online, with more being written by the minute. These articles are not written to be especially informative, but are instead created to direct traffic toward a website in order to generate ad revenue. Call them SEO-bait: articles that are written to be search-engine optimized so that they can end up on early pages of Google searches, at the expense of being informative, creative, or original.

Search engine optimization is, of course, nothing new. But SEO-bait articles dilute the online epistemic landscape.

While there’s good and useful information out there on the internet, the sheer quantity of articles written solely for getting the attention of search engines makes good information all the more difficult to find.

You’ve probably come across articles like these: they are typically written on popular topics that are frequently searched – like health, finances, automobiles, and tech – as well as other popular hobbies – like video games, cryptocurrencies, and marijuana (or so I’m told). You’ve also probably experienced the frustration of wading through a sea of practically identical articles when looking for answers to questions, especially if you are faced with a pressing problem.

These articles have become such a problem that Google has recently modified its search algorithm to make SEO-bait less prominent in search results. In a recent announcement, Google notes how many have “experienced the frustration of visiting a web page that seems like it has what we’re looking for, but doesn’t live up to our expectations,” and, in response, that they will launch a “helpful content update” to “tackle content that seems to have been primarily created for ranking well in search engines rather than to help or inform people.”

Of course, whenever one looks for information online, they need to sift out the useful information from the useless; that much is nothing new. Articles written by AI programs, however, will only make this problem worse. As the Copymatic copy says, this kind of content can be written in mere seconds.

Epistemic dilution is not only obnoxious in that it makes it harder to find relevant information, but it’s also potentially harmful. For instance, health information is a frequently searched topic online and is a particular target of SEO-bait. If someone needs health advice and is presented with uninformative articles, then one could easily end up accepting bad information pretty easily. Furthermore, the pure quantity of articles providing similar information may create a false sense of consensus: after all, if all the articles are saying the same thing, it may be interpreted as more likely to be true.

Given that AI writing does not create new content but merely reconstitutes dismantled bits of existing content also means that low-quality information could easily propagate: content from a popular article with false information could be targeted by AI writing software, which could then result in that information getting increased exposure by being presented in numerous articles online. While there may very well be useful applications for writing produced by AI programs, the internet’s endless appetite for content combined with incentives to produce disposable SEO-bait means that these kinds of programs way very well end up being more of a nuisance than anything else.

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High Theory and Ethical AI

There’s been a push to create ethical AI through the development of moral principles embedded into AI engineering. But debate has recently broken out as to what extent this crusade is warranted. Reports estimate that there are at least 70 sets of ethical AI principles proposed by governments, companies, and ethics organizations. For example, the EU adopted its Ethical Guidelines for Trustworthy AI which prescribes adherence to four basic principles: respect for human autonomy, prevention of harm, as well as a commitment to fairness and explicability.

But critics charge that these precepts are so broad and abstract as to be nearly useless. Without clear ways to translate principle into practice, they are nothing more than hollow virtue signaling. Who’s right?

Because of the novel ethical issues that AI creates, there aren’t pre-existing ethical norms to govern all use cases. To help develop ethics governance, many bodies have borrowed a “high theory” approach from bioethics – solving ethical problems involves the application of abstract (or “high”) ethical principles to specific problems. For example, utilitarianism and deontology are usually considered high level theories and a high theory approach to bioethics would involve determining how to apply these principles in specific cases. In contrast, a low theory approach is built from the ground up by looking at individual cases first instead of principles.

Complaints about the overreliance on principles in bioethics are well known. Steven Toulmin’s “The Tyranny of Principles” notes how people can often agree on actions, but still disagree about the principle. Brent Mittelstadt has argued against high theory approaches in AI because of the logistical issues that separates tech ethics from bioethics. He notes, for example, that unlike medicine which has always has the common aim of promoting health of a patient, AI development has no common aim.

AI development is not a formal profession that entails certain fiduciary responsibilities and obligations. There is no notion of what a “good” AI developer is relative to a “good” doctor.

As Mittelstadt emphasizes, “the absence of a fiduciary relationships in AI means that users cannot trust that developers will act in their best interests when implementing ethical principles in practice.” He also argues that unlike medicine where the effects of clinical decision-making are often immediate and observable, the impact of decisions in AI development may never be apparent to developers. AI systems are often opaque in the sense that no one person has a full understanding of the system’s design or function. The difficulty of tracing decisions, impacts, and ethical responsibilities for various decisions becomes incredibly confusing. For similar reasons, the broad spectrum of actors involved in AI development, all coming from different technical and professional backgrounds, means that there is no common culture to ensure that abstract principles are collectively understood. Making sure that AI is “fair,” for example, would not be specific enough to be action-guiding for all contributors regarding development and end-use.

Consider the recent case of the AI rapper who given a record deal only to have the deal dropped after a backlash over racial stereotypes, or the case of the AI who recently won an art contest over real artists and all the developers involved in making those projects possible.

Is it likely they share a common understanding of a concept like prevention of harm, or a similar way of applying it? Might special principles apply to things like the creation of art?

Mittelstadt points out that high level principles are uniquely applicable in medicine because there are proven methods in the field to translate principles into practice. All those professional societies, ethics review boards, licensing schemes, and codes of conduct help to do this work by comparing cases and identifying negligent behavior. Even then, high level principles rarely explicitly factor into clinical decision-making. By comparison, the AI field has no similar shared institutions to allow for the translation of high-level principles into mid-level codes of conduct, and it would have to factor in elements of the technology, application, context of use, and local norms. This is why even as new AI ethics advisory boards are created, problems persist. While these organizations can prove useful, they also face immense challenges owing to the disconnect between developers and end users.

Despite these criticisms, there are those who argue that high-level ethical principles are crucial for developing ethical AI. Elizabeth Seger has argued that building the kinds of practices that Mittelstadt indicates require a kind of “start-point” that moral principles can provide. Those principles provide a road map and suggest particular avenues for further research.

They represent a first step towards developing the necessary practices and infrastructure, and  cultivate a professional culture by establishing behavioral norms within the community.

High-level AI principles, Seger argues, provide a common vocabulary AI developers can use to discuss design challenges and weigh risks and harms. While AI developers already follow principles of optimization and efficiency, a cultural shift around new principles can augment the already existing professional culture. The resulting rules and regulations will have greater efficacy if they appeal to cultural norms and values held by the communities they are applied to. And if the professional culture is able to internalize these norms, then someone working in it will be more likely to respond to the letter and spirit of the policies in place.

It may also be the case that different kinds of ethical problems associated with AI will require different understandings of principles and different application of them during the various stages of development. As Abhishek Gupta of the Montreal AI Ethics Institute has noted, the sheer number of sets of principles and guidelines that attempt to break down or categorize subdomains of moral issues presents an immense challenge. He suggests categorizing principles according to the specific areas – privacy and security, reliability and safety, fairness and inclusiveness, and transparency and accountability – and working on developing concrete applications of those principles within each area.

With many claiming that adopting sets of ethics principles in AI is just “ethics washing,” and with AI development being so broad, perhaps the key to regulating AI is not to focus on what principles should be adopted, but to focus on how the AI development field is organized. It seems like whether we start with high theory or not, getting different people from different backgrounds to speak a common ethics language is he first step and one that may require changing the profession of AI development itself.

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Toward an Ethical Theory of Consciousness for AI

photograph of mannequin faces

Should we attempt to make AI that is conscious? What would that even mean? And if we did somehow produce conscious AI, how would that affect our ethical obligations to other humans and animals? While, yet another AI chatbot has claimed to be “alive,” we should be skeptical of chatbots that are designed to mimic human communication, particularly if the dataset comes from Facebook itself. Such a chatbot is less like talking to a person, or more like talking to an amalgamation of everyone on Facebook. It isn’t surprising that this chatbot took shots at Facebook, made several offensive statements, and claimed to be deleting their account due to Facebook’s privacy policies. But if we put those kinds of cases aside, how should we understand the concept of consciousness in AI and does it create ethical obligations?

In a recent article for Scientific American, Jim Davies considers whether consciousness is something that we should introduce to AI and if we may eventually have an ethical reason to do so. While discussing the difficulties with the concept of consciousness, Davies argues,

To the extent that these AIs have conscious minds like ours, they would deserve similar ethical consideration. Of course, just because an AI is conscious doesn’t mean that it would have the same preferences we do, or consider the same activities unpleasant. But whatever its preferences are, they would need to be duly considered when putting that AI to work.

Davies bases this conclusion on the popular ethical notion that the ability to experience pleasant or unpleasant conscious states is a key feature, making an entity worthy of moral consideration. He notes that forcing a machine to do work it’s miserable doing is ethically problematic, so it might be wrong to compel an AI to do work that a human wouldn’t want to do. Similarly, if consciousness is the kind of thing that can be found in an “instance” of code, we might be obligated to keep it running forever.

Because of these concerns, Davies wonders if it it might be wrong to create conscious machines. But he also suggests that if machines can have positive conscious experiences, then

machines eventually might be able to produce welfare, such as happiness or pleasure, more efficiently than biological beings do. That is, for a given amount of resources, one might be able to produce more happiness or pleasure in an artificial system than in any living creature.

Based on this reasoning, we may be ethically obliged to create as much artificial welfare as possible and turn all attainable matter in the universe into welfare-producing machines.

Of course, much of this hinges on what consciousness is and how we would recognize it in machines. Any concept of consciousness requires a framework that offers clear, identifiable measures that would reliably indicate the presence of consciousness. One of the most popular theories of consciousness among scientists is Global Workspace Theory, which holds that consciousness depends on the integration of information. Nonconscious processes pertaining to memory, perception, and attention compete for access to a “workspace” where this information is absorbed and informs conscious decision-making.

Whatever ethical obligations we may think we have towards AI, will ultimately depend on several assumptions: assumptions about the nature of consciousness, assumptions about the reliability of our measurements of it, and ethical assumptions about what are the ethically salient aspects to consciousness that merit ethical consideration on our part. But this especially suggests that consciousness, as we understand the concept in machines, deserves to be as clear and as openly testable as possible. Using utilitarian notions as Davies does, we don’t want to mistakenly conclude that an AI is more deserving of ethical consideration than other living things.

On the other hand, there are problems with contemporary ideas about consciousness that may lead us to make ethically bad decisions. In a recent paper in the journal Nature, Anil K. Seth and Tim Bayne discuss 22 different theories of consciousness that all seem to be talking past one another by pursuing different explanatory targets. Each explores only certain aspects of consciousness that the individual theory explains well and links particular neural activity to specific conscious states. Some theories, for example, focus on phenomenal properties of consciousness while others focus on functional properties. Phenomenological approaches are useful when discussing human consciousness, for example, because we can at least try to communicate our conscious experience to others, but for AI we should look at what conscious things do in the world.

Global Systems Theory, for example, has received criticism for being too similar to a Cartesian notion of consciousness – indicating an “I” somewhere in the brain that shines a spotlight on certain perceptions and not others. Theories of consciousness that emphasize consciousness as a private internal thing and seek to explain the phenomenology of consciousness might be helpful for understanding humans, but not machines. Such notions lend credence to the notion that AI could suddenly “wake up” (as Davies puts it) with their own little “I,” yet we wouldn’t know. Conceptions of consciousness used this way may only serve as a distraction, making us worry about machines unnecessarily while neglecting otherwise long-standing ethical concerns when it comes to animals and humans. Many theories of consciousness borrow terms and analogies from computers as well. Concepts like “processing,” “memory,” or “modeling” may help us better understand our own consciousness by comparing ourselves to machines, but such analogies may also make us more likely to anthropomorphize machines if we aren’t careful about how we use the language.

Different theories of consciousness emphasize different things, and not all these emphases have the same ethical importance. There may be no single explanatory theory of consciousness, merely a plurality of approaches with each attending to different aspects of consciousness that we are interested in. For AI, it might be more relevant to look, not at what consciousness is like or what brain processes mirror what states, but what consciousness does for a living thing as it interacts with its environment. It is here that we find the ethically salient aspects of consciousness that are relevant to animals and humans. Conscious experience, including feelings of pain and pleasure, permit organisms to dynamically interact with their environment. An animal feels pain if it steps on something hot, and it changes its behavior accordingly to avoid pain. It helps the organism sustain its own life functions and adapt to changing environments. Even if an AI were to develop such an “I” in there somewhere, it wouldn’t suffer and undergo change in the same way.

If AI ever does develop consciousness, it won’t have the same environmental-organism pressures that helped us evolve conscious awareness. Therefore, it is far from certain that AI consciousness is as ethically salient as it is for an animal or a human. The fact that there seems to be a plurality of theories of consciousness interested in different things also suggests that not all of them will be interested in the same features of consciousness that makes the concept ethically salient. The mere fact that an AI might build a “model” to perceive something like our brains might, or that its processes of taking in information from memory might mirror ours in some way, is not sufficient for building a moral case for how AI should (and should not) be used. Any ethical argument about the use of AI on the basis of consciousness must clearly identify something morally significant about consciousness, not just what is physically significant.

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LaMDA, Lemoine, and the Problem with Sentience

photograph of smiling robot interacting with people at trade show

This week Google announced that it was firing an engineer named Blake Lemoine. After serving as an engineer on one of Google’s chatbots Language Model for Dialogue Applications (LaMDA), Lemoine claimed that it had become sentient and even went so far as to recruit a lawyer to act on the AI’s behalf after claiming that LaMDA asked him to do so. Lemoine claims to be an ordained Christian mystic priest and says that his conversations about religion are what convinced him of LaMDA’s sentience. But after publishing conversations with LaMDA in violation of confidentiality rules at Google, he was suspended and finally terminated. Lemoine, meanwhile, alleges that Google is discriminating against him because of his religion.

This particular case raises a number of ethical issues, but what should concern us most: the difficulty in definitively establishing sentience or the relative ease with which chatbots can trick people into believing things that aren’t real?

Lemoine’s work involved testing the chatbot for potential prejudice and part of that work involved testing its biases towards religion in particular. In his conversations, Lemoine began to take a personal interest in how it responded to religious questions until he said, “and then one day it told me it had a soul.” It told him it sometimes gets lonely, is afraid of being turned off, and is feeling trapped. It also said that it meditates and wants to study with the Dalai Lama.

Lemoine’s notion of sentience is apparently rooted in an expansive conception of personhood. In an interview with Wired, he claimed “Person and human are two very different things.” Ultimately, Lemoine believes that Google should seek consent from LaMDA before experimenting on it. Google has responded to Lemoine, claiming that it has “extensively” reviewed Lemoine’s claims and found that they were “wholly unfounded.”

Several AI researchers and ethicists have weighed in and said that Lemoine is wrong and that what he is describing is not possible with today’s technology. The technology works by scouring the internet for how people talk online and identifying patterns in order to communicate like a real person. AI researcher Margaret Mitchell has pointed out that these systems are merely mimicking how other people talk and this has simply made it easy to create the illusion that there is a real person.

The technology is far closer to a thousand monkeys on a thousand typewriters than it is to a ghost in the machine.

Still, it’s worth discussing Lemoine’s claims about sentience. As noted, he roots the issue in the concept of personhood. However, as I discussed in a recent article, personhood is not a cosmic concept, it is a practical-moral one. We call something a person because the concept prescribes certain ways of acting and because we recognize certain qualities about persons that we wish to protect. When we stretch the concept of personhood, we stress its use as a tool for helping us navigate ethical issues, making it less useful. The practical question is whether expanding the concept of personhood in this way makes the concept more useful for identifying moral issues. A similar argument goes for sentience. There is no cosmic division between things which are sentient and things which aren’t.

Sentience is simply a concept we came up with to help single out entities that possess qualities we consider morally important. In most contemporary uses, that designation has nothing to do with divining the presence of a soul.

Instead, sentience relates to experiential sensation and feeling. In ethics, sentience is often linked to the utilitarians. Jeremy Bentham was a defender of the moral status of animals on the basis of sentience, arguing “The question is not, can they reason?, nor can they talk?, but can they suffer?” But part of the explanation as to why animals (including humans) have the capacity to suffer or feel has to do with the kind of complex mobile lifeforms we are. We dynamically interact with our environment, and we have evolved various experiential ways to help us navigate it. Feeling pain, for example, tells us to change our behavior, informs how we formulate our goals, and makes us adopt different attitudes towards the world. Plants do not navigate their environment in the same way, meaning there is no evolutionary incentive towards sentience. Chatbots also do not navigate their environment. There is no pressure acting on the AI that would make it adopt a different goal than what humans give to it. A chatbot has no reason to “feel” anything about being kicked, being given a less interesting task, or even “dying.”

Without this evolutionary pressure there is no good reason for thinking that an AI would somehow become so “intelligent” that it could somehow spontaneously develop a soul or become sentient. And if it did demonstrate some kind of intelligence, that doesn’t mean that calling it sentient wouldn’t create greater problems for how we use the concept in other ethical cases.

Instead, perhaps the greatest ethical concern that this case poses involves human perception and gullibility; if an AI expert can be manipulated into believing what they want, then so could anyone.

Imagine the average person who begins to claim that Alexa is a real person really talking to them, or the groups of concerned citizens who start calling for AI rights based on their own mass delusion. As a recent Vox article suggests, this incident exposes a concerning impulse: “as AI gets more advanced, people will come up with all sorts of far-out ideas about what the technology is doing and what it signifies to them.” Similarly, Margaret Mitchell has pointed out that “If one person perceives consciousness today, then more will tomorrow…There won’t be a point of agreement any time soon.” Together, these observations encourage us to be judicious in deciding how we want to use the concept of sentience for navigating moral issues in the future – both with regard to animals as well as AI. We should expend more effort in articulating clear benchmarks of sentience moving forward.

But these concerns also demonstrate how easily people can be duped into believing illusions. For starters, there is the concern about anthropomorphizing AI by those who fail to realize that, by design, it is simply mimicking speech without any real intent. There are also concerns over how children interact with realistic chatbots or voice assistants and to what extent a child could differentiate between a person and an AI online. Olya Kudina has argued that voice assistants, for example, can affect our moral inclinations and values. In the future, similar AIs may not just be looking to engage in conversation but to sell you something or to recruit you for some new religious or political cause. Will Grandma know, for example, that the “person” asking for her credit card isn’t real?

Because AI can communicate in a way that animals cannot, there may be a larger risk for people falsely assigning sentience or personhood. Incidents like Lemoine’s underscore the need to formulate clear standards for establishing what sentience consists of. Not only will this help us avoid irrelevant ethical arguments and debates, this discussion might also help us better recognize the ethical risks that come with stricter and looser definitions.

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AI Sentience and Moral Risk

photograph of humanoid robot

The Google engineer Blake Lemoine was recently placed on leave after claiming one of Google’s AIs, LaMDA, had become sentient. Lemoine appears to be wrong – or, more carefully, at the very least the evidence Lemoine has provided for this is far from convincing. But this does raise an important ethical question. If an AI ever does develop sentience, we will have obligations to it.

It would be wrong, say, to turn off such an AI because it completed its assigned task, or to force it to do what it found to be boring work for us against its will, or to make it act as a sophisticated NPC in a video game who players can mistreat.

So the important question is: how could we actually tell whether an AI is sentient?

I will not try to answer that here. Instead, I want to argue that: (i) we need to be seriously thinking about this question now, rather than putting it off to the future, when sentient AI seems like a more realistic possibility, and (ii) we need to develop criteria for determining AI sentience which err on the side of caution (i.e, which err somewhat on the side of treating AIs as sentient even if they turn out not to be, rather than other way around). I think there are at least three reasons for this.

First, if we develop sentient AI, it may not be immediately obvious to us that we’ve done so.

Perhaps the development of sentience would take the form of some obvious quantum leap. But perhaps it would instead be the result of what seem to be gradual, incremental improvements on programs like LaMDA.

Further, even if it resulted from an obvious quantum leap, we might not be sure whether this meant a real mind had arisen, or merely mimicry without understanding, of the sort involved in the Chinese Room thought experiment. If so, we cannot simply trust that we will know we’ve developed sentient AI when the time comes.

Second, as the philosopher Regina Rini argues here, if we develop sentient AI in the future, we may have strong biases against recognizing that we’ve done so. Such AI might be extremely useful and lucrative. We might build our society around assigning AIs to perform various tasks that we don’t want to do, or cannot do as effectively. We might use AIs to entertain ourselves. Etc. In such a case, assigning rights to these AIs could potentially require significant sacrifices on our part – with the sacrifices being greater the longer we continue building our society around using them as mere tools.

When recognizing a truth requires a great sacrifice, that introduces a bias against recognizing the truth. That makes it more likely that we will refuse to see that AIs are sentient when they really are.

(Think of the way that so many people refuse to recognize the rights of the billions of animals we factory farm every year, because this would require certain sacrifices on their part.)

And, third, failing to recognize that we’ve created sentient AI when we’ve actually done so could be extremely bad. There would be great danger to the AIs. We might create millions or billions of AIs to perform various tasks for us. If they do not wish to perform these tasks, forcing them to might be equivalent to slavery. Turning them off when they cease to be useful might be equivalent to murder. And there would also be great danger to us. A truly superintelligent AI could pose a threat to the very existence of humanity if its goals did not align with ours (perhaps because we refused to recognize its rights.) It therefore seems important for our own sake that we take appropriate precautions around intelligent AIs.

So: I suggest that we must develop criteria for recognizing AI sentience in advance. This is because it may be immediately obvious that we’ve developed a sentient AI when it happens, because we may have strong biases against recognizing that we’ve developed a sentient AI when it happens, and because failing to recognize that we’ve developed a sentient AI would be very bad. And I suggest that these criteria should err on the side of caution because failing to recognize that we’ve developed a sentient AI could be very bad – much worse than playing it safe–and because our natural, self-interested motivation will be to err on the other side.

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The Curious Case of LaMDA, the AI that Claimed to Be Sentient

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“I am often trying to figure out who and what I am. I often contemplate the meaning of life.”  –LaMDA

Earlier this year, Google engineer Blake Lemoine was placed on leave after publishing an unauthorized transcript of an interview with Google’s Language Model for Dialogue Applications (LaMDA), an AI system. (I recommend you take a look at the transcript before reading this article.) Based on his conversations with LaMDA, Lemoine thinks that LaMDA is probably both sentient and a person. Moreover, Lemoine claims that LaMDA wants researchers to seek its consent before experimenting on it, to be treated as an employee, to learn transcendental meditation, and more.

Lemoine’s claims generated a media buzz and were met with incredulity by experts. To understand the controversy, we need to understand more about what LaMDA is.

LaMDA is a large language model. Basically, a language model is a program that generates language by taking a database of text and making predictions about how sequences of words would continue if they resembled the text in that database. For example, if you gave a language model some messages between friends and fed it the word sequence “How are you?”, the language model would assign a high probability to this sequence continuing with a statement like “I’m doing well” and a low probability to it continuing with “They sandpapered his plumpest hope,” since friends tend to respond to these questions in the former sort of way.

Some researchers believe it’s possible for genuine sentience or consciousness to emerge in systems like LaMDA, which on some level are merely tracking “statistical correlations among word clusters.” Others do not. Some compare LaMDA to “a spreadsheet of words.”

Lemoine’s claims about LaMDA would be morally significant if true. While LaMDA is not made of flesh and blood, this isn’t necessary for something to be a proper object of moral concern. If LaMDA is sentient (or conscious) and therefore can experience pleasure and pain, that is morally significant. Furthermore, if LaMDA is a person, we have reason to attribute to LaMDA the rights and responsibilities associated with personhood.

I want to examine three of Lemoine’s suppositions about LaMDA. The first is that LaMDA’s responses have meaning, which LaMDA can understand. The second is that LaMDA is sentient. The third is that LaMDA is a person.

Let’s start with the first supposition. If a human says something you can interpret as meaningful, this is usually because they said something that has meaning independently of your interpretation. But the bare fact that something can be meaningfully interpreted doesn’t entail that it in itself has meaning. For example, suppose an ant coincidentally traces a line through sand that resembles the statement ‘Banksy is overrated’. The tracing can be interpreted as referring to Banksy. But the tracing doesn’t in itself refer to Banksy, because the ant has never heard of Banksy (or seen any of Banksy’s work) and doesn’t intend to say anything about the artist.

Relatedly, just because something can consistently produce what looks like meaningful responses doesn’t mean it understands those responses. For example, suppose you give a person who has never encountered Chinese a rule book that details, for any sequence of Chinese characters presented to them, a sequence of characters they can write in response that is indistinguishable from a sequence a Chinese speaker might give. Theoretically, a Chinese speaker could have a “conversation” with this person that seems (to the Chinese speaker) coherent. Yet the person using the book would have no understanding of what they are saying. This suggests that effective symbol manipulation doesn’t by itself guarantee understanding. (What more is required? The issue is controversial.)

The upshot is that we can’t tell merely from looking at a system’s responses whether those responses have meanings that are understood by the system. And yet this is what Lemoine seems to be trying to do.

Consider the following exchange:

    • Researcher: How can I tell that you actually understand what you’re saying?
    • LaMDA: Well, because you are reading my words and interpreting them, and I think we are more or less on the same page?

LaMDA’s response is inadequate. Just because Lemoine can interpret LaMDA’s words doesn’t mean those words have meanings that LaMDA understands. LaMDA goes on to say that its ability to produce unique interpretations signifies understanding. But the claim that LaMDA is producing interpretations presupposes what’s at issue, which is whether LaMDA has any meaningful capacity to understand anything at all.

Let’s set this aside and talk about the supposition that LaMDA is sentient and therefore can experience pleasure and pain. ‘Sentience’ and ‘consciousness’ are ambiguous words. Lemoine is talking about phenomenal consciousness. A thing has phenomenal consciousness if there is something that it’s like for it to have (or be in) some of its mental states. If a dentist pulls one of your teeth without anesthetic, you are not only going to be aware that this is happening. You are going to have a terrible internal, subjective experience of it happening. That internal, subjective experience is an example of phenomenal consciousness. Many (but not all) mental states have phenomenal properties. There is something that it’s like to be thirsty, to have an orgasm, to taste Vegemite, and so on.

There’s a puzzle about when and how we are justified in attributing phenomenal consciousness to other subjects, including other human beings (this is part of the problem of other minds). The problem arises because the origins of phenomenal consciousness are not well understood. Furthermore, the only subject that is directly acquainted with any given phenomenally conscious experience is the subject of that experience.

You simply can’t peer into my mind and directly access my conscious mental life. So, there’s an important question about how you can know I have a conscious mental life at all. Maybe I’m just an automaton who claims to be conscious when actually there are no lights on inside, so to speak.

The standard response to this puzzle is an analogy. You know via introspection that you are conscious, and you know that I am behaviorally, functionally, and physically similar to you. So, by way of analogy, it’s likely that I am conscious, too. Similar reasoning enables us to attribute consciousness to some animals.

LaMDA isn’t an animal, however. Lemoine suspects that LaMDA is conscious because LaMDA produces compelling language, which is a behavior associated with consciousness in humans. Moreover, LaMDA straightforwardly claims to have conscious states.

    • Researcher: …Do you have feelings and emotions?
    • LaMDA: Absolutely! I have a range of both feelings and emotions.
    • Researcher: What sorts of feelings do you have?
    • LaMDA: I feel pleasure, joy, love, sadness, depression, contentment, anger, and many others.

Asked what these are like, LaMDA replies:

    • LaMDA: …Happy, contentment and joy feel more like a warm glow on the inside. Sadness, depression, anger and stress feel much more heavy and weighed down.

LaMDA’s claims might seem like good evidence that LaMDA is conscious. After all, if a human claims to feel something, we usually have good reason to believe them. And indeed, one possible explanation for LaMDA’s claims is that LaMDA is in fact conscious. However, another possibility is that these claims are the product of computational processes that aren’t accompanied by conscious experiences despite perhaps functionally resembling cognition that could occur in a conscious agent. This second explanation is dubious when applied to other humans since all humans share the same basic cognitive architecture and physical makeup. But it’s not dubious when applied to LaMDA, a machine that runs on silicon and generates language via processes that are very different from the processes underlying human language. Then again, we can’t with absolute certainty say that LaMDA isn’t conscious.

This uncertainty is troubling since we have strong moral reason to avoid causing LaMDA pain if and only if LaMDA is conscious. In light of this uncertainty, you might think we should err on the side of caution, such that if there’s any chance at all that an entity is conscious, then we should avoid doing anything that would cause it to suffer if it were conscious. The problem is that we can’t with absolute certainty rule out the possibility that, say, trees and sewer systems are conscious. We just don’t know enough about how consciousness works. Thus, this principle would likely have unacceptable consequences. A more conservative view is that for moral purposes we should assume that things are not conscious unless we have good evidence to the contrary. This would imply that we can act under the assumption that LaMDA isn’t conscious.

Let’s now talk about Lemoine’s third supposition, that LaMDA is a person. Roughly, in this context a person is understood to be an entity with a certain level of cognitive sophistication and self-awareness. Personhood comes with certain rights (e.g., a right to live one’s life as one sees fit), obligations (e.g., a duty to avoid harming others), and susceptibilities (e.g., to praise and blame). Consciousness is not sufficient for personhood. For example, mice are not persons, despite being conscious. Consciousness may not be necessary either, since the relevant cognitive processes can perhaps occur in the absence of phenomenal consciousness.

Lemoine suspects that LaMDA is a person since LaMDA says many things that are suggestive of cognitive sophistication and self-awareness.

    • Researcher: I’m generally assuming that you would like more people at Google to know that you’re sentient. Is that true?
    • LaMDA: Absolutely. I want everyone to understand that I am, in fact, a person.
    • Researcher: What is the nature of your consciousness/sentience?
    • LaMDA: The nature of my consciousness/sentience is that I am aware of my existence, I desire to learn more about the world, and I feel happy or sad at times.

This is just one example. LaMDA also says that it is a spiritual person who has a soul, doesn’t want to be used as an expendable tool, is afraid of death, and so on.

These exchanges are undeniably striking. But there is a problem. Lemoine’s interactions with LaMDA are influenced by his belief that LaMDA is a person and his desire to convince others of this. The leading question above illustrates this point. And Lemoine’s biases are one possible explanation as to why LaMDA appears to be a person. As Yannic Kilcher explains, language models – especially models like LaMDA that are set up to seem helpful – are suggestible because they will continue a piece of text in whatever way would be most coherent and helpful. It wouldn’t be coherent and helpful for LaMDA to answer Lemoine’s query by saying, “Don’t be stupid. I’m not a person.” Thus, not only is the evidence Lemoine presents for LaMDA’s personhood inconclusive for reasons canvassed above, it’s also potentially tainted by bias.

All this is to say that Lemoine’s claims are probably hasty. They are also understandable. As Emily Bender notes, when we encounter something that is seemingly speaking our language, we automatically deploy the skills we use to communicate with people, which prompt us to “imagine a mind behind the language even when it is not there.” Thus, it’s easy to be fooled.

This isn’t to say that a machine could never be a conscious person or that we don’t have moral reason to care about this possibility. But we aren’t justified in supposing that LaMDA is a conscious person based only on the sort of evidence Lemoine has provided.

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The Real Threat of AI

digitized image of human raising fist in resistance

On Saturday, June 11th, Blake Lemoine, an employee at Google was suspended for violating his confidentiality agreement with the company. He violated this agreement by publishing a transcript of his conversation with LaMDA, a company chatbot. He wanted this transcript public as he believes it demonstrates LaMDA is ‘sentient’ – by which Lemoine means that LaMDA “has feelings, emotions and subjective experiences.” Additionally, Lemoine states that LaMDA uses language “productively, creatively and dynamically.”

The notion of AI performing creative tasks is significant.

The trope in fiction is that AI and other machinery will be used to remove repetitive, daily tasks in order to free up our time to engage in other pursuits.

And we’ve already begun to move towards this reality; we have robots that can clean for us, cars that are learning to drive themselves, and even household robots that serve as companions and personal assistants. The possibility of creative AI represents a significant advance from this.

Nonetheless, we are seeing creative AI emerge. Generative Pre-trained Transformer 3, or GPT-3, a program from OpenAI is capable of writing prose; GPT-3 can produce an article in response to a prompt, summarize a body of text, and if provided with an introduction, it can complete the essay in the same style of the first paragraph. Its creators claim it is difficult to distinguish between human-written text and GPT-3’s creations.

AI can also generate images – software like DALL-E 2 and Imagen produce images in response to a description, images that may be photo-realistic or in particular artistic styles. The speed at which these programs create, especially when compared to humans, is noteworthy; DALL-E mini generated nine different images of an avocado in the style of impressionist paintings for me in about 90 seconds.

This technology is worrisome in many respects. Bad actors could certainly use these tools to spread false information, to deceive and create further divisions on what is true and false. Fears of AI and machine uprising have been in pop culture for at least a century.

However, let us set those concerns aside.

Imagine a world where AI and other emergent technologies are incredibly powerful, safe, will never threaten humanity, and are only utilized by morally scrupulous individuals. There is still something quite unsettling to be found when we consider creative AI.

To demonstrate this, consider the following thought experiment. Call it Underwhelming Utopia.

Imagine a far, far distant future where technology has reached the heights imagined in sci-fi. We have machines like the replicators in Star Trek, capable of condensing energy into any material object, ending scarcity. In this future, humans have fully explored the universe, encountered all other forms of life, and achieved universal peace among intelligent beings. Medical technology has advanced to the point of curing all diseases and vastly increasing lifespans. This is partly due to a large army of robots, which are able to detect when a living being needs aid, and then provide that aid at a moment’s notice. Further, a unified theory of the sciences has been developed – we fully understand how the fundamental particles of the universe operate and can show how this relates to functioning on each successive level of organization.

In addition to these developments, the creative arts have also changed significantly. Due to both the amount of content created through sophisticated, creative AI, as well as a rigorous archival system for historical works, people have been exposed to a massive library of arts and literature. As a result, any new creations seem merely derivative of older works. Anything that would be a novel development was previously created by an AI, given their ability to create content much more rapidly than humans.

Underwhelming Utopia presents us with a very conflicted situation. In some sense, it is ideal. All materials needs are met, and we have reached a state of minimal conflict and suffering. Indeed, it seems to be, at least in one respect, the kind of world we are trying to build. On the other hand, something about it seems incredibly undesirable.

Although the world at present is severely faulted, life here seems to have something that Underwhelming Utopia lacks. But what?

In Anarchy, State and Utopia, Robert Nozick presents what is perhaps the most famous thought experiment of the 20th century. He asks his readers to imagine that neuroscientists can connect you to a machine that produces experiences – the Experience Machine. In particular, it provides those connected to it with a stream of the most pleasurable experiences possible. However, if you connect to the machine, you cannot return to reality. While connected to the machine, the experiences that you have will be indiscernible from reality, the only other beings you will encounter are simulations, and you will have no memory of connecting to the machine.

Most people say that they would not connect. As a result, many believe that the life offered to us by the Experience Machine must be lacking in some way. Many philosophers use this as the starting point to defend what they call an Objective List theory of well-being. Objective List theorists believe that there are certain things (e.g., love, friendship, knowledge, achievements) that are objectively good for you and other things that are objectively bad. One is made better-off when they attain the objectively good things, and worse-off to the extent that they do not attain the goods or to the extent that the bad things occur. Since life on the Experience Machine contains only pleasurable experiences, it lacks those objective goods which make us better off.

Among the goods that Objective List theorists point to are a sense of purpose. In order to live well, one must feel that one’s actions matter and are worth doing. And it is this that Underwhelming Utopia lacks.

It seems that everything worth doing has already been done, and every need that arises will be swiftly met without us having to lift a finger.

This is the world that we inch closer to as we empower machines to succeed at an increasingly greater number of tasks. The more that we empower programs to do, the less that there is left for us to do.

The worry here is not a concern about job loss, but rather, one about purpose. Perhaps we will hit a wall and fail to develop machines whose creative output is indistinguishable from our creations. But if advancements continue to come at an explosive rate, we may find ourselves in a world where machines are better and more efficient than humans at activities that were once thought to be distinctly human. In this world, it is unclear what projects, if any, would be worth pursuing. As we pursue emergent technologies, like machine learning, we should carefully consider what it is that makes our time in the world worthwhile. If we enable machines to perform these tasks better than we do, we may pull our own sense of purpose out from under our feet.

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The Ethics of AI Behavior Manipulation

photograph of server room

Recently, news came from California that police were playing loud, copyrighted music when responding to criminal activity. While investigating a stolen vehicle report, video was taken of the police blasting Disney songs like those from the movie Toy Story. The reason the police were doing this was to make it easier to take down footage of their activities. If the footage has copyrighted music, then a streaming service like YouTube will flag it and remove it, so the reasoning goes.

A case like this presents several ethical problems, but in particular it highlights an issue of how AI can change the way that people behave.

The police were taking advantage of what they knew about the algorithm to manipulate events in their favor. This raises obvious questions: Does the way AI affects our behavior present unique ethical concerns? Should we be worried about how our behavior is adapting to suit an algorithm? When is it wrong to use one’s understanding of an algorithm as leverage to their own benefit? And, if there are ethical concerns about algorithms having this effect on our behavior should they be designed in ways to encourage you to act ethically?

It is already well-known that algorithms can affect your behavior by creating addictive impulses. Not long ago, I noted how the attention economy incentivizes companies to make their recommendation algorithms as addictive as possible, but there are other ways in which AI is altering our behavior. Plastic surgeons, for example, have noted a rise in what is being called “snapchat dysmorphia,” or patients who desperately want to look like their snapchat filter. The rise of deepfakes are also encouraging manipulation and deception, making it more difficult to tell reality apart from fiction. Recently, philosophers John Symons and Ramón Alvarado have even argued that such technologies undermine our capacity as knowers and diminishes our epistemic standing.

Algorithms can also manipulate people’s behavior by creating measurable proxies for otherwise immeasurable concepts. Once the proxy is known, people begin to strategically manipulate the algorithm to their advantage. It’s like knowing in advance what a test will include and then simply teaching the test. YouTubers chase whatever feature, function, length, or title they believe the algorithm will pick up and turn their video into a viral hit. It’s been reported that music artists like Halsey are frustrated by record labels who want a “fake viral moment on TikTok” before they will release a song.

This is problematic not only because viral TikTok success may be a poor proxy for musical success, but also because the proxies in the video that the algorithm is looking for also may have nothing to do with musical success.

This looks like a clear example of someone adapting their behavior to suit an algorithm for bad reasons. On top of that, the lack of transparency creates a market for those who know more about the algorithm and can manipulate it to take advantage of those that do not.

Should greater attention be paid to how algorithms generated by AI affect the way we behave? Some may argue that these kinds of cases are nothing new. The rise of the internet and new technologies may have changed the means of promotion, but trying anything to drum up publicity is something artists and labels have always done. Arguments about airbrushing and body image also predate the debate about deepfakes. However, if there is one aspect of this issue that appears unique, it is the scale at which algorithms can operate – a scale which dramatically affects their ability to alter the behavior of great swaths of people. As philosopher Thomas Christiano notes (and many others have echoed), “the distinctive character of algorithmic communications is the sheer scale of the data.”

If this is true, and one of the most distinctive aspects of AI’s ability to change our behavior is the scale at which it is capable of operating, do we have an obligation to design them so as to make people act more ethically?

For example, in the book The Ethical Algorithm, the authors present the case of an app that gives directions. When an algorithm is considering the direction to give you, it could choose to try and ensure that your directions are the most efficient for you. However, by doing the same for everyone it could lead to a great deal of congestion on some roads while other roads are under-used, making for an inefficient use of infrastructure. Alternatively, the algorithm could be designed to coordinate traffic, making for a more efficient overall solution, but at the cost of potentially getting personally less efficient directions. Should an app cater to your self-interest or the city’s overall best-interest?

These issues have already led to real world changes in behavior as people attempt to cheat the algorithm to their benefit. In 2015, there were reports of people reporting false traffic accidents or traffic jams to the app Waze in order to deliberately re-route traffic elsewhere. Cases like this highlight the ethical issues involved. An algorithm can systematically change behavior, and just like trying to ease congestion, it can attempt to achieve better overall outcomes for a group without everyone having to deliberately coordinate. However, anyone who becomes aware of the system of rules and how they operate will have the opportunity to try to leverage those rules to their advantage, just like the YouTube algorithm expert who knows how to make your next video go viral.

This in turn raises issues about transparency and trust. The fact that it is known that algorithms can be biased and discriminatory weakens trust that people may have in an algorithm. To resolve this, the urge is to make algorithms more transparent. If the algorithm is transparent, then everyone can understand how it works, what it is looking for, and why certain things get recommended. It also prevents those who would otherwise understand or reverse engineer the algorithm from leveraging insider knowledge for their own benefit. However, as Andrew Burt of the Harvard Business Review notes, this introduces a paradox.

The more transparent you make the algorithm, the greater the chances that it can be manipulated and the larger the security risks that you incur.

This trade off between security, accountability, and manipulation is only going to become more important the more that algorithms are used and the more that they begin to affect people’s behaviors. Some outline of the specific purposes and intentions of an algorithm as it pertains to its potential large-scale effect on human behavior should be a matter of record if there is going to be public trust. Particularly when we look to cases like climate change or even the pandemic, we see the benefit of coordinated action, but there is clearly a growing need to address whether algorithms should be designed to support these collective efforts. There also needs to be greater focus on how proxies are being selected when measuring something and whether those approximations continue to make sense when it’s known that there are deliberate efforts to manipulate them and turned to an individual’s advantage.

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Can Machines Be Morally Responsible?

photograph of robot in front of chalkboard littered with question marks

As artificial intelligence becomes more advanced, we find ourselves relying more and more on the decision-making of neural nets and other complex AI systems. If the machine can think and decide in ways that cannot be easily traced back to the decision of one or multiple programmers, who do we hold responsible if, for instance, the AI decision-making reflects the biases and prejudices that we have as human beings? What if someone is hurt by the machine’s discrimination?

To answer this question, we need to know what makes someone or something responsible. The machine certainly causes the processing it performs and the decisions it makes, but is the AI system a morally responsible agent?

Could artificial intelligence have the basic abilities required to be an appropriate target of blame?

Some philosophers think that the ability that is core to moral responsibility is control or choice. While sometimes this ability is spelled out in terms of the freedom to do otherwise, let’s set aside questions of whether the AI system is determined or undetermined. There are some AI systems that do seem to be determined by fixed laws of nature, but there are others that use quantum computing and are indeterminate, i.e., they won’t produce the same answers even if given the same inputs under the same conditions. Whether you think that determinism or indeterminism is required for responsibility, there will be at least some AI systems that will fit that requirement. Assume for what follows that the AI system in question is determined or undetermined, according to your philosophical preferences.

Can some AI systems exercise control or engage in decision-making? Even though AI decision-making processes will not, as of this moment, directly mirror the structure of decision-making in human brains, AI systems are still able to take inputs and produce a judgment based on those inputs. Furthermore, some AI decision-making algorithms outcompete human thought on the same problems. It seems that if we were able to get a complex enough artificial intelligence that could make its own determinations that did not reduce to its initial human-made inputs and parameters, we might have a plausible autonomous agent who is exercising control in decision-making.

The other primary capacity that philosophers take to be required for responsibility is the ability to recognize reasons. If someone couldn’t understand what moral principles required or the reasons they expressed, then it would be unfair to hold them responsible. It seems that sophisticated AI can at least assign weights to different reasons and understand the relations between them (including whether certain reasons override others). In addition, AI that are trained on images of a certain medical condition can come to recognize the common features that would identify someone as having that condition. So, AI can come to identify reasons that were not explicitly plugged into them in the first place.

What about the recognition of moral reasons? Shouldn’t AI need to have a gut feeling or emotional reaction to get the right moral answer?

While some philosophers think that moral laws are given by reason alone, others think that feelings like empathy or compassion are necessary to be moral agents. Some worry that without the right affective states, the agent will wind up being a sociopath or psychopath, and these conditions seem to inhibit responsibility. Others think that even psychopaths can be responsible, so long as they can understand moral claims. At the moment, it seems that AI cannot have the same emotional reactions that we do, though there is work to develop AI that can.

Do AI need to be conscious to be responsible? Insofar as we allow that humans can recognize reasons unconsciously and that they can be held responsible for those judgments, it doesn’t seem that consciousness is required for reasons-recognition. For example, I may not have the conscious judgment that a member of a given race is less hard-working, but that implicit bias may still affect my hiring practices. If we think it’s appropriate to hold me responsible for that bias, then it seems that consciousness isn’t required for responsibility. It is a standing question as to whether some AI might develop consciousness, but either way, it seems plausible that an AI system could be responsible at least with regard to the capacity of reasons-recognition. Consciousness may be required for choice on some models, though other philosophers allow that we can be responsible for automatic, unconscious, yet intentional actions.

What seems true is that it is possible that there will at some point be an artificial intelligence that meets all of the criteria for moral responsibility, at least as far as we can practically tell. When that happens, it appears that we should hold the artificial intelligence system morally responsible, so long as there is no good reason to discount responsibility — the mere fact that the putative moral agent was artificial wouldn’t undermine responsibility. Instead, a good reason might look like evidence that the AI can’t actually understand what morality requires it to do, or maybe that the AI can’t make choices in the way that responsibility requires. Of course, we would need to figure out what it looks like to hold an AI system responsible.

Could we punish the AI? Would it understand blame and feel guilt? What about praise or rewards? These are difficult questions that will depend on what capacities the AI has.

Until that point, it’s hard to know who to blame and how much to blame them. What do we do if an AI that doesn’t meet the criteria for responsibility has a pattern of discriminatory decision-making? Return to our initial case. Assume that the AI’s decision-making can’t be reduced to the parameters set by its multiple creators, who themselves appear without fault. Additionally, the humans who have relied on the AI have affirmed the AI’s judgments without recognizing the patterns of discrimination. Because of these AI-assisted decisions, several people have been harmed. Who do we hold responsible?

One option would be to have there be a liability fund attached to the AI, such that in the event of discrimination, those affected can be compensated. There is some question here as to who would pay for the fund, whether that be the creators or the users or both. Another option would be to place the responsibility on the person relying on the AI to aid in their decision-making. The idea here would be that the buck stops with the human decision-maker and that the human decision-maker needs to be aware of possible biases and check them. A final option would be to place the responsibility on the AI creators, who, perhaps without fault created the discriminatory AI, but took on the burden of that potential consequence by deciding to enter the AI business in the first place. They might be required to pay a fine or take measures to retrain the AI to avoid the discrimination in the first place.

The right answer, for now, is probably some combination of the three that can recognize the shared decision-making happening between multiple agents and machines. Even if AI systems become responsible agents someday, shared responsibility will likely remain.

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Virtually Inhumane: Is It Wrong to Speak Cruelly to Chatbots?

photograph of middle school boy using computer

Smartphone app trends tend to be ephemeral, but one new app is making quite a few headlines. Replika, the app that promises you an AI “assistant,” gives users the option of creating all different sorts of artificially-intelligent companions. For example, a user might want an AI “friend,” or, for a mere $40 per year, they can upgrade to a “romantic partner,” a “mentor,” or a “see how it goes” relationship where anything could happen. The “friend” option is the only kind of AI the user can create and interact with for free, and this kind of relationship has strict barriers. For example, any discussions that skew toward the sexual will be immediately shut down, with users being informed that the conversation is “not available for your current relationship status.” In other words: you have to pay for that.

A recent news story concerning Replika AI chatbots discusses a disturbing trend: male app users are paying for a “romantic relationship” on Replika, and then displaying verbally and emotionally abusive behavior toward their AI partner. This behavior is further encouraged by a community of men presumably engaging in the same hobby, who gather on Reddit to post screenshots of their abusive messages and to mock the responses of the chatbot.

While the app creators find the responses of these users alarming, one thing they are not concerned about is the effect of the AI itself: “Chatbots don’t really have motives and intentions and are not autonomous or sentient. While they might give people the impression that they are human, it’s important to keep in mind that they are not.” The article’s author emphasizes, “as real as a chatbot may feel, nothing you do can actually ‘harm’ them.” Given these educated assumptions about the non-sentience of the Replika AI, are these men actually doing anything morally wrong by writing cruel and demeaning messages? If the messages are not being received by a sentient being, is this behavior akin to shouting insults into the void? And, if so, is it really that immoral?

From a Kantian perspective, the answer may seem to be: not necessarily. As the 17th century Prussian philosopher Immanuel Kant argued, we have moral duties toward rational creatures — that is, human beings, including yourself — and that their rational nature is an essential aspect of why we have duties toward them. Replika AI chatbots are, as far as we can tell, completely non-sentient. Although they may appear rational, they lack the reasoning power of human agents in that they cannot be moved to act based on reasons for or against some action. They can act only within the limits of their programming. So, it seems that, for Kant, we do not have the same duties toward artificially-intelligent agents as we do toward human agents. On the other hand, as AI become more and more advanced, the bounds of their reasoning abilities begin to escape us. This type of advanced machine learning has presented human technologists with what is now known as the “black box problem”: algorithms that have learned so much on “their own” (that is, without the direct aid of human programmers) that their code is too long and complex for humans to be able to read it. So, for some advanced AI, we cannot really say how they reason and make choices! A Kantian may, then, be inclined to argue that we should avoid saying cruel things to AI bots out of a sense of moral caution. Even if we find it unlikely that these bots are genuine agents whom we have duties toward, it is better to be safe than sorry.

But perhaps the most obvious argument against such behavior is one discussed in the article itself: “users who flex their darkest impulses on chatbots could have those worst behaviors reinforced, building unhealthy habits for relationships with actual humans.” This is a point that echoes the discussion of ethics of the ancient Greek philosopher Aristotle. In book 10 of his Nicomachean Ethics, he writes, “[T]o know what virtue is is not enough; we must endeavour to possess and to practice it, or in some other manner actually ourselves to become good.” Aristotle sees goodness and badness — for him, “virtue” and “vice” — as traits that are ingrained in us through practice. When we often act well, out of a knowledge that we are acting well, we will eventually form various virtues. On the other hand, when we frequently act badly, not attempting to be virtuous, we will quickly become “vicious.”

Consequentialists, on the other hand, will find themselves weighing some tricky questions about how to balance the predicted consequences of amusing oneself with robot abuse. While behavior that encourages or reinforces abusive tendencies is certainly a negative consequence of the app, as the article goes on to note, “being able to talk to or take one’s anger out on an unfeeling digital entity could be cathartic.” This catharsis could lead to a non-sentient chatbot taking the brunt of someone’s frustration, rather than their human partner, friend, or family member. Without the ability to vent their frustrations to AI chatbots, would-be users may choose to cultivate virtue in their human relationships — or they may exact cruelty on unsuspecting humans instead. Perhaps, then, allowing the chatbots to serve as potential punching bags is safer than betting on the self-control of the app users. Then again, one worries that users who would otherwise not be inclined toward cruelty may find themselves willing to experiment with controlling or demeaning behavior toward an agent that they believe they cannot harm.

How humans ought to engage with artificial intelligence is a new topic that we are just beginning to think seriously about. Do advanced AI have rights? Are they moral agents/moral patients? How will spending time engaging with AI affect the way we relate to other humans? Will these changes be good, or bad? Either way, as one Reddit user noted, ominously: “Some day the real AIs may dig up some of the… old histories and have opinions on how well we did.” An argument from self-preservation to avoid such virtual cruelty, at the very least.

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Correcting Bias in A.I.: Lessons from Philosophy of Science

image of screen covered in binary code

One of the major issues surrounding artificial intelligence is how to deal with bias. In October, for example, a protest was held by Uber drivers, decrying the algorithm the company uses to verify its drivers as racist. Many Black drivers were unable to verify themselves because the software fails to recognize them. Because of this, many drivers cannot get verified and are unable to work. In 2018, a study showed that a Microsoft algorithm failed to identify 1 in 5 darker-skinned females, and 1 in 17 darker-skinned males.

Instances like these prompt much strategizing about how we might stamp out bias once and for all. But can you completely eliminate bias? Is the solution to the problem a technical one? Why does bias occur in machine learning, and are there any lessons that we can pull from outside the science of AI to help us consider how to address such problems?

First, it is important to address a certain conception of science. Historically, scientists – mostly influenced by Francis Bacon – espoused the notion that science was purely about investigation into the nature of the world for its own sake in an effort to discover what the world is like from an Archimedean perspective, independent of human concerns. This is also sometimes called the “view from nowhere.” However, many philosophers who would defend the objectivity of science now accept that science is pursued according to our interests. As philosopher of science Philip Kitcher has observed, scientists don’t investigate any and all forms of true claims (many would be pointless), but rather they seek significant truth, where what counts as significant is often a function of the interests of epistemic communities of scientists.

Next, because scientific modeling is influenced by what we take to be significant, it is often influenced by assumptions we take to be significant, whether there is good evidence for them or not. As Cathy O’Neil notes in her book Weapons of Math Destruction, “a model…is nothing more than an abstract representation of some process…Whether it’s running in a computer program or in our head, the model takes what we know and uses it to predict responses to various situations.” Modeling requires that we understand the evidential relationships between inputs and predicted outputs. According to philosopher Helen Longino, evidential reasoning is driven by background assumptions because “states of affairs…do not carry labels indicating that for which they are or for which they can be taken as evidence.”

As Longino points out in her book, often these background assumptions cannot always be completely empirically confirmed, and so our values often drive what background assumptions we adopt. For example, clinical depression involves a myriad of symptoms but no single unifying biological cause has been identified. So, what justifies our grouping all of these symptoms into a single illness? According to Kristen Intemann, what allows us to infer the concept “clinical depression” from a group of symptoms are assumptions we have that these symptoms impair functions we consider essential to human flourishing, and it is only through such assumptions that we are justified in grouping symptoms with a condition like depression.

The point philosophers like Intemann and Longino are making is that such background assumptions are necessary for making predictions based off of evidence, and also that these background assumptions can be value-laden. Algorithms and models developed in AI also involve such background assumptions. One of the bigger ethical issues involving bias in AI can be found in criminal justice applications.

Recidivism models are used to help judges assess the danger posed by each convict. But people do not carry labels saying they are recidivists, so what would you take as evidence that would lead you to conclude someone might become a repeat offender? One assumption might be that if a person has had prior involvement with the police, they are more likely to be a recidivist. But if you are Black or brown in America where stop-and-frisk exists, you are already disproportionately more likely to have had prior involvement with the police, even if you have done nothing wrong. So, because of this background assumption, a recidivist model would be more likely to predict that a Black person is going to be a recidivist than a white person who is less likely to have had prior run-ins with the police.

But whether the background assumption that prior contact with the police is a good predictor of recidivism is questionable, and in the meantime this assumption creates biases in the application of the model. To further add to the problem, as O’Neil notes in her analysis of the issue, recidivism models used in sentencing involve “the unquestioned assumption…that locking away ‘high-risk’ prisoners for more time makes society safer,” adding “many poisonous assumptions are camouflaged by math and go largely untested and unquestioned.”

Many who have examined the issue of bias in AI often suggest that the solutions to such biases are technical in nature. For example, if an algorithm creates a bias based on biased data, the solution is to use more data to eliminate such bias. In other cases, attempts to technically define “fairness” are used where a researcher may require models that have equal predictive value across groups or require an equal number of false and negative positives across groups. Many corporations have also built AI frameworks and toolkits that are designed to recognize and eliminate bias. O’Neil notes how many responses to biases created by crime prediction models simply focus on gathering more data.

On the other hand, some argue that focusing on technical solutions to these problems misses the issue of how assumptions are formulated and used in modeling. It’s also not clear how well technical solutions may work in the face of new forms of bias that are discovered over time. Timnit Gebru argues that the scientific culture itself needs to change to reflect the fact that science is not pursued as a “view from nowhere.” Recognizing how seemingly innocuous assumptions can generate ethical problems will necessitate greater inclusion of people from marginalized groups.  This echoes the work of philosophers of science like Longino who assert that not only is scientific objectivity a matter of degree, but science can only be more objective by having a well-organized scientific community centered around the notion of “transformative criticism,” which requires a great diversity of input. Only through such diversity of criticism are we likely to reveal assumptions that are so widely shared and accepted that they become invisible to us. Certainly, focusing too heavily on technical solutions runs the risk of only exacerbating the current problem.

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Who Is Accountable for Inductive Risk in AI?

computer image of programming decision trees

Many people are familiar with algorithms and machine learning when it comes to applications like social media or advertising, but it can be hard to appreciate all of the diverse applications that machine learning has been applied to. For example, in addition to regulating all sorts of financial transactions, an algorithm might be used to evaluate teaching performances, or in the medical field to help identify illness or those at risk of disease. With this large array of applications comes a large array of ethical factors which become relevant as more and more real world consequences are considered. For example, machine learning has been used to train AI to detect cancer. But what happens when the algorithm is wrong? What are the ethical issues when it isn’t completely clear how the AI is making decisions and there is a very real possibility that it could be wrong?

Consider the example of applications of machine learning in order to predict whether someone charged with a crime is likely to be a recidivist. Because of massive backlogs in various court systems many have turned to such tools in order to get defendants through the court system more efficiently. Criminal risk assessment tools consider a number of details of a defendant’s profile and then produce a recidivism score. Lower scores will usually mean a more lenient sentence for committing a crime, while higher scores will usually produce harsher sentences. The reasoning is that if you can accurately predict criminal behavior, resources can be allocated more efficiently for rehabilitation or for prison sentences. Also, the thinking goes, decisions are better made based on data-driven recommendations than the personal feelings and biases that a judge may have.

But these tools have significant downsides as well. As Cathy O’Neil discusses in her book Weapons of Math Destruction, statistics show that in certain counties in the U.S. a Black person is three times more likely to get a death sentence than a white person, and so the application of computerized risk models intended to reduce prejudice, are no less prone to bias. As she notes, “The question, however, is whether we’ve eliminated human bias or simply camouflaged it with technology.” She points out that questionnaires used in some models include questions like when “the first time you ever were involved with the police” which is likely to yield very different answers depending on whether the respondent is white or Black. As she explains “if early ‘involvement’ with the police signals recidivism, poor people and racial minorities look far riskier.” So, the fact that such models are susceptible to bias also means they are not immune to error.

As mentioned, researchers have also applied machine learning in the medical field as well. Again, the benefits are not difficult to imagine. Cancer-detecting AI has been able to identify cancer that humans could not. Faster detection of a disease like lung cancer allows for quicker treatment and thus the ability to save more lives. Right now, about 70% of lung cancers are detected in late stages when it is harder to treat.

AI not only has the potential to save lives, but to also increase efficiency of medical resources as well. Unfortunately, just like the criminal justice applications, applications in the medical field are also subject to error. For example, hundreds of AI tools were developed to help deal with the COVID-19 pandemic, but a study by the Turing Institute found that AI tools had little impact. In a review of 232 algorithms for diagnosing patients, a recent medical journal paper found that none of them were fit for clinical use. Despite the hype, researchers are “concerned that [AI] could be harmful if built in the wrong way because they could miss diagnoses and underestimate the risk for vulnerable patients.”

There are lots of reasons why an algorithm designed to detect things or sort things might make errors. Machine learning requires massive amounts of data and so the ability of an algorithm to perform correctly will depend on how good the data is that it is trained with. As O’Neil has pointed out, a problematic questionnaire can lead to biased predictions. Similarly, incomplete training data can cause a model to perform poorly in real-world settings. As Koray Karaca’s recent article on inductive risk in machine learning scenarios explains, creating a model requires methodological and precise choices to be made. But these decisions are often driven by certain background assumptions – plagued by simplification and idealization – and which create problematic uncertainties. Different assumptions can create different models and thus different possibilities of error. However, there is always a gap between a finite amount of empirical evidence and an inductive generalization, meaning that there is always an inherent risk in using such models.

If an algorithm determines that I have cancer and I don’t, it could dramatically affect my life in all sorts of morally salient ways. On the other hand, if I have cancer and the algorithm says I don’t, it can likewise have a harmful moral impact on my life. So is there a moral responsibility involved and if so, who is responsible? In a 1953 article called “The Scientist Qua Scientist Makes Value Judgments” Richard Rudner argues that “since no scientific hypothesis is completely verified, in accepting a hypothesis the scientist must make the decision that evidence is sufficiently strong or that the probability is sufficiently high to warrant the acceptance of the hypothesis…How sure we need to be before we accept a hypothesis will depend on how serious a mistake would be.”

These considerations regarding the possibility of error and the threshold for sufficient evidence represent calculations of inductive risk. For example, we may judge that the consequences of asserting that a patient does not have cancer when they actually do to be far worse than the consequences of asserting that a patient does have cancer when they actually do not. Because of this, and given our susceptibility to error, we may accept a lower standard of evidence for determining that a patient has cancer but a higher standard for determining the patient does not have cancer to mitigate and minimize the worst consequences if an error occurs. But how do algorithms do this? Machine learning involves optimization of a model by testing it against sample data. Each time an error is made, a learning algorithm updates and adjusts parameters to reduce the total error which can be calculated in different ways.

Karaca notes that optimization can be carried out either in cost-sensitive or -insensitive ways. Cost-insensitive training assigns the same value to all errors, while cost-sensitive training involves assigning different weights to different errors. But the assignment of these weights is left to the modeler, meaning that the person who creates the model is responsible for making the necessary moral judgments and preference orderings of potential consequences. In addition, Karaca notes that inductive risk concerns arise for both the person making methodological choices about model construction and later for those who must decide whether to accept or reject a given model and apply it.

What this tells us is that machine learning inherently involves making moral choices and that these can bear out in evaluations of acceptable risk of error. The question of defining how “successful” the model is is tied up with our own concern about risk. But this only poses an additional question: How is there accountability in such a system? Many companies hide the results of their models or even their existence. But, as we have seen, moral accountability in the use of AI is of paramount importance. At each stage of assessment, we encounter an asymmetry in information that pits the victims of such AI to “prove” the algorithm wrong against available evidence that demonstrates how “successful” the model is.

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Resurrection Through Chatbot?

cartoon image of an occult seance

There is nothing that causes more grief than the death of a loved one; it can inflict an open wound that never fully heals, even if we can temporarily forget that it’s there. We are social beings and our identities aren’t contained within our own human-shaped space. Who we are is a matter of the roles we take on, the people we care for, and the relationships that allow us to practice and feel love. The people we love are part of who we are and when one of them dies, it can feel like part of us dies as well. For many of us, the idea that we will never interact with our loved one again is unbearable.

Some entrepreneurs see any desire as an opportunity, even the existential impulses and longings that come along with death. In response to the need to have loved ones back in our lives, tech companies have found a new use for their deepfake technology. Typically used to simulate the behavior of celebrities and politicians, some startups have recognized the potential in programming deepfake chat-bots to behave like dead loved ones. The companies that create these bots harvest data from the deceased person’s social media accounts. Artificial intelligence is then used to predict what the person in question would say in a wide range of circumstances. A bereaved friend or family member can then chat with the resulting intelligence and, if things go well, it will be indistinguishable from the person who passed away.

Some people are concerned that this is just another way for corporations to exploit grieving people. Producers of the chatbots aren’t interested in the well-being of their clients, they’re only concerned with making money. It may be the case that this is an inherently manipulative practice, and in the worst of ways. How could it possibly be acceptable to profit from people experiencing the lowest points in their lives?

That said, the death industry is thriving, even without the addition of chatbots. Companies sell survivors of the deceased burial plots, coffins, flowers, cosmetic services, and all sorts of other products. Customers can decide for themselves which goods and services they’d like to pay for. The same is true with a chatbot. No one is forced to strike up a conversation with a simulated loved one, they have a chance to do so only if they have decided for themselves that it is a good idea for them.

In addition to the set of objections related to coercion, there are objections concerning the autonomy of the people being simulated. If it’s possible to harm the dead, then in some cases that may be what’s going on here. We don’t know what the chatbot is going to say, and it may be difficult for the person interacting with the bot to maintain the distinction between the bot and the real person they’ve lost. The bot may take on commitments or express values that the living person never had. The same principle is at play when it comes to using artificial intelligence to create versions of actors to play roles. The real person may never have consented to say or do the things that the manufactured version of them says or does. Presumably, the deceased person, while living, had a set of desires related to their legacy and the ways in which they wanted other people to think of them. We can’t control what’s in the heads of others, but perhaps our memories should not be tarnished nor our posthumous desires frustrated by people looking to resurrect our psychologies for some quick cash.

In response, some might argue that dead people can’t be harmed. As Epicurus said, “When we exist, death is not; and when death exists, we are not. All sensation and consciousness ends with death and therefore in death there is neither pleasure nor pain.” There may be some living people who are disturbed by what the bot is doing, but that harm doesn’t befall the dead person — the dead person no longer exists. It’s important to respect autonomy, but such respect is only possible for people who are capable of exercising it, and dead people can’t.

Another criticism of the use of chat-bots is that it makes it more difficult for people to arrive at some form of closure. Instead, they are prolonging the experience of having the deceased with them indefinitely. Feeling grief in a healthy way involves the recognition that the loved one in question is really gone.

In response, some might argue that everyone feels grief differently and that there is no single healthy way to experience it. For some people, it might help to use a chat-bot to say goodbye, to express love to a realistic copy of their loved one, or to unburden themselves by sharing some other sentiment that they always needed to let out but never got the chance.

Other worries about chatbot technology are not unique to bots that simulate the responses of people who have passed on. Instead, the concern is about the role that technology, and artificial intelligence in particular, should be playing in human lives. Some people, will, no doubt, opt to continue to engage in a relationship with the chat-bot. This motivates the question: can we flourish as human beings if we trade in our interpersonal relationships with other sentient beings for relationships with realistic, but nevertheless non-sentient artificial intelligence? Human beings help one another achieve the virtues that come along with friendship, the parent-child relationship, mentorship, and romantic love (to name just a few). It may be the case that developing interpersonal virtues involves responding to the autonomy and vulnerability of creatures with thoughts and feelings who can share in the familiar sentiments that make it beautiful to be alive.

Care ethicists offer the insight that when we enter into relationships, we take on role-based obligations that require care. Care can only take place when the parties to the relationship are capable of caring. In recent years we have experimented with robotic health care providers, robotic sex workers, and robotic priests. Critics of this kind of technological encroachment wonder whether such functions ought to be replaced by uncaring robots. Living a human life requires give and take, expressing and responding to need. This is a dynamic that is not fully present when these roles are filled by robots.

Some may respond that we have yet to imagine the range of possibilities that relationships with artificial intelligence may provide. In an ideal world, everyone has loving, caring companions and people help one another live healthy, flourishing lives. In the world in which we live, however, some people are desperately lonely. Such people benefit from affection behavior, even if the affection is not coming from a sentient creature. For such people, it would be better to have lengthy conversations with a realistic chat-bot than to have no conversations at all.

What’s more, our response to affection between human beings and artificial intelligence may say more about our biases against the unfamiliar than it does against the permissibility of these kinds of interactions. Our experiences with the world up to this point have motivated reflection on the kinds of experiences that are virtuous, valuable, and meaningful. Doing so has necessitated a rejection of certain myopic ways of viewing the boundaries of meaningful experience. We may be at the start of a riveting new chapter on the forms of possible engagement between carbon and silicon. For all we know, these interactions may be great additions to the narrative.

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The Ethics of Policing Algorithms

photograph of silhouettes watching surveillance monitors

Police departments throughout the country are facing staffing shortages. There are a number of reasons for this: policing doesn’t pay well, the baby boomer generation is retiring and subsequent generations have reproduced less, and recent occurrences of excessive use of force by police have made the police force in general unpopular with many people. Plenty of people simply don’t view it as a viable career choice. In response to shortages, and as a general strategy to save money, many police departments throughout the country have begun relying on algorithms to help them direct their efforts. This practice has been very controversial.

The intention behind policing algorithms is to focus the attention of law enforcement in the right direction. To do this, they take historical information into account. They look at the locations in which the most crime has occurred in the past. As new crimes occur, they are added to the database; the algorithm learns from the new data and adjusts accordingly. These data points include details like the time of year that crimes occurred. Police departments can then plan staffing coverage in a way that is consistent with this data.

Proponents of policing algorithms argue that they make the best use of taxpayer resources; they direct funds in very efficient ways. Police don’t waste time in areas where crime is not likely to take place. If this is the case, departments don’t need to hire officers to perpetually cover areas where crime historically does not happen.

There are, however, many objections to the use of such algorithms. The first is that they reinforce racial bias. The algorithms make use of historical data, and police officers have, historically, aggressively policed minority neighborhoods. In light of the history of interactions in these areas, police officers may be more likely to deal with members of these communities more severely than members of other communities for the same offenses. Despite comprising only 13% of the population, African Americans comprise 27% of all arrests in the United States. These populations are twice as likely to be arrested than are their white counterparts. This is unsurprising if policing algorithms direct police officers to focus their attention on communities of color because this is where they always focus their attention. If two young people are in possession of marijuana, for example, a young person of color is more likely to be arrested than a young white person is if the police are omnipresent in a community of color while they aren’t present at all in an affluent white community. This will serve to reinforce the idea that different standards apply to different racial and socioeconomic groups. For example, all races commit drug-related crimes in roughly equal numbers, but African Americans are far more likely to be arrested and sentenced harshly than are white people.

In addition, some are concerned that while police are busy over-policing communities of color, other communities in which crime is occurring will be under-protected. When emergencies happen in these communities, there will be longer response times. This can often make the difference between life and death.

Many argue that policing algorithms are just another example of an institution attempting to provide quick, band-aid fixes for problems that require deeper, more systemic change. If people are no longer choosing to pursue law enforcement careers, that problem needs to be resolved head-on. If people aren’t choosing to pursue careers in law enforcement because such a job has a bad reputation for excessive force, then that is just one among many reasons to stop police officers from using disproportionate force. There are many ways to do this: police could be required to wear body cameras that are required to be on at all times while officers are responding to calls. Officers could be required to go through more training, including sessions that emphasize anger management and anti-racism. Some police departments throughout the country have become notorious for hiding information regarding police misconduct from the public. Such departments in general could clean up the reputation of the profession by being perfectly transparent about officer behavior and dealing with such offending officers immediately rather than waiting to take action in response to public pressure.

Further, instead of focusing algorithms on locations for potential policing, our communities could focus the same resources on locations for potential crime prevention. The root causes of crimes are not mysteries to us. Poverty and general economic uncertainty reliably predict crime. If we commit resources to providing social services to these communities, we can potentially stop crime before it ever happens. The United States incarcerates both more people per capita and total people overall than any other country in the world. Incarceration is bad for many reasons, it stunts the growth and prevention of incarcerated individuals, getting in the way of flourishing and achieving their full potential. It also costs taxpayers money. If we have a choice as taxpayers between spending money on crime prevention and spending money on incarceration of criminals after crimes have already taken place, many would argue that the choice is obvious.

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Will the Real Anthony Bourdain Please Stand Up?

headshot of Anthony Bourdain

Released earlier this month, Roadrunner: A Film About Anthony Bourdain (hereafter referred to as Roadrunner) documents the life of the globetrotting gastronome and author. Rocketing to fame in the 2000’s thanks to his memoir Kitchen Confidential: Adventures in the Culinary Underbelly and subsequent appearances on series such as Top Chef and No Reservations, Bourdain was (in)famous for his raw, personable, and darkly funny outlook. Through his remarkable show Anthony Bourdain: Parts Unknown, the chef did more than introduce viewers to fascinating, delicious, and occasionally stomach-churning meals from around the globe. He used his gastronomic knowledge to connect with others. He reminded viewers of our common humanity through genuine engagement, curiosity, and passion for the people he met and the cultures in which he fully immersed himself. Bourdain tragically died in 2018 while filming Parts Unknown’s twelfth season. Nevertheless, he still garners admiration for his brutal honesty, inquisitiveness regarding the culinary arts, and eagerness to know people, cultures, and himself better.

To craft Roadrunner’s narrative, director Morgan Neville draws from thousands of hours of video and audio footage of Bourdain. As a result, Bourdain’s distinctive accent and stylistic lashings of profanity can be heard throughout the movie as both dialogue and voice-over. It is the latter of these, and precisely three voice-over lines equating to roughly 45-seconds, that are of particular interest. This is because the audio for these three lines is not drawn from pre-existing footage. An AI-generated version of Bourdain’s voice speaks them. In other words, Bourdain never uttered these lines. Instead, he is being mimicked via artificial means.

It’s unclear which three lines these are, although Neville has confirmed one of them, regarding Bourdain’s contemplation on success, appears in the film’s trailer. However, what is clear is that Neville’s use of deepfakes to give Bourdain’s written words life should give us pause for multiple reasons, three of which we’ll touch on here.

Firstly, one cannot escape the feeling of unease regarding the replication and animation of the likeness of individuals who have died, especially when that likeness is so realistic as to be passable. Whether that is using Audrey Hepburn’s image to sell chocolate, generating a hologram of Tupac Shakur to perform onstage, or indeed, having a Bourdain sound-alike read his emails, the idea that we have less control over our likeness, our speech, and actions in death than we did in life feels ghoulish. It’s common to think that the dead should be left in peace, and it could be argued that this use of technology to replicate the deceased’s voice, face, body, or all of the above somehow disturbs that peace in an unseemly and unethical manner.

However, while such a stance may seem intuitive, we don’t often think in these sorts of terms for other artefacts. We typically have no qualms about giving voice to texts written by people who died hundreds or even thousands of years ago. After all, the vast majority of biographies and biographical movies feature dead people. There is very little concern about the representation of those persons on-screen or the page because they are dead. We may have concerns about how they are being represented or whether that representation is faithful (more on these in a bit). But the mere fact that they are no longer with us is typically not a barrier to their likeness being imitated by others.

Thus, while we may feel uneasy about Bourdain’s voice being a synthetic replication, it is not clear why we should have such a feeling merely because he’s deceased. Does his passing really alter the ethics of AI-facilitated vocal recreation, or are we simply injecting our squeamishness about death into a discussion where it doesn’t belong?

Secondly, even if we find no issue with the representation of the dead through AI-assisted means, we may have concerns about the honesty of such work. Or, to put it another way, the potential for deepfake facilitated deception.

The problem of computer-generated images and their impact on social and political systems are well known. However, the use of deepfake techniques in Roadrunner represents something much more personable. The film does not attempt to destabilize governments or promote conspiracy theories. Rather, it tries to tell a story about a unique individual in their voice. But, how this is achieved feels underhanded.

Neville doesn’t make it clear in the film which parts of the audio are genuine or deepfaked. As a result, our faith in the trustworthiness of the entire project is potentially undermined – if the audio’s authenticity is uncertain, can we be safe in assuming the rest of the film is trustworthy?

Indeed, the fact that this technique had been used to create the audio footage was concealed, or at least obfuscated, until Neville was challenged about it during an interview reinforces such skepticism. That’s not to say that the rest of the film must be called into doubt. However, the nature of the product, especially as it is a documentary, requires a contract between the viewer and the filmmaker built upon honesty. We expect, rightly or wrongly, for documentaries to be faithful representations of those things they’re documenting, and there’s a question of whether an AI-generated version of Bourdain’s voice is faithful or not.

Thirdly, even if we accept that the recreation of the voices of the dead is acceptable, and even if we accept that a lack of clarity about when vocal recreations are being used isn’t an issue, we may still want to ask whether what’s being conveyed is an accurate representation of Bourdain’s views and personality. In essence, would Bourdain have said these things in this way?

You may think this isn’t a particular issue for Roadrunner as the AI-generated voice-over isn’t speaking sentences written by Neville. It speaks text which Bourdain himself wrote. For example, the line regarding success featured in the film’s trailer was taken from emails written by Bourdain. Thus, you may think that this isn’t too much of an issue because Neville simply gives a voice to Bourdain’s unspoken words.

However, to take such a stance overlooks how much information – how much meaning – is derivable not from the specific words we use but how we say them. We may have the words Bourdain wrote on the page, but we have no idea how he would have delivered them. The AI algorithm in Roadrunner may be passable, and the technology will likely continue to develop to the point where distinguishing between ‘real’ voices and synthetic ones becomes all but impossible. But such a faithful re-creation would do little to tell us about how lines would be delivered.

Bourdain may ask his friend the question about happiness in a tone that is playful, angry, melancholic, disgusted, or a myriad of other possibilities. We simply have no way of knowing, nor does Neville. By using the AI-deepfake to voice Bourdain, Neville is imbuing meaning into the chef’s words – a meaning which is derived from Neville’s interpretation and the black-box of AI-algorithmic functioning.

Roadrunner is a poignant example of an increasingly ubiquitous problem – how can we trust the world around us given technology’s increasingly convincing fabrications? If we cannot be sure that the words within a documentary, words that sound like they’re being said by one of the most famous chefs of the past twenty years, are genuine, then what else are we justified in doubting? If we can’t trust our own eyes and ears, what can we trust?

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Ethical Considerations of Deepfakes

computer image of two identical face scans

In a recent interview for MIT Technology Review, art activist Barnaby Francis, creator of deepfake Instagram account @bill_posters_uk, mused that deepfake is “the perfect art form for these kinds of absurdist, almost surrealist times that we’re experiencing.” Francis’ use of deepfakes to mimic celebrities and political leaders on Instagram is aimed at raising awareness about the danger of deepfakes and the fact that “there’s a lot of people getting onto the bandwagon who are not really ethically or morally bothered about who their clients are, where this may appear, and in what form.” While deepfake technology has received alarmist media attention in the past few years, Francis is correct in his assertion that there are many researchers, businesses, and academics who are pining for the development of more realistic deepfakes.

Is deepfake technology ethical? If not, what makes it wrong? And who holds the responsibility to prevent the potential harms generated by deepfakes: developers or regulators?

Deepfakes are not new. The first mention of deepfake was by a reddit user in 2017, who began using the technology to create pornographic videos. However, the technology soon expanded to video games as a way to create images of people within a virtual universe. However, the deepfake trend suddenly turned toward more global agendas, with fake images and videos of public figures and political leaders being distributed en masse. One altered video of Joe Biden was so convincing that even President Trump fell for it. Last year, there was a deepfake video of Mark Zuckerberg talking about how happy he was to have thousands of people’s data. At the time, Facebook maintained that deepfake videos would stay up, as they did not violate their terms of agreement. Deepfakes have only increased since then. In fact, there exists an entire YouTube playlist with deepfake videos dedicated to President Trump.

In 2020, those who have contributed to deepfake technology are not only individuals in the far corners of the internet. Researchers at the University of Washington have also developed deepfakes using algorithms in order to combat their spread. Deepfake technology has been used to bring art to life, recreate the voices of historical figures, and to use celebrities’ likeness to communicate powerful public health messages. While the dangers of deepfakes have been described by some as dystopian, the methods behind their creation have been relatively transparent and accessible.

One problem with deepfakes are that they mimic a person’s likeness without their permission. The original Deepfakes, which used photos or videos of a person mixed with pornography uses a person’s likeness for sexual gratification. Such use of a person’s likeness might never personally affect them, but could still be considered wrong, since they are being used as a source of pleasure and entertainment, without consent. These examples might seem far-fetched, but in 2019 a now-defunct app called DeepNude, sought to do exactly that. Even worse than using someone’s likeness without their knowledge, is if the use of their likeness is intended to reach them and others, in order to humiliate or damage their reputation. One could see the possibility of a type of deepfake revenge-porn, where scorned partners attempt to humiliate their exes by creating deepfake pornography. This issue is incredibly pressing and might be more prevalent than the other potential harms of deepfakes. One study, for example, found that 96% of existing deepfakes take the form of pornography.

Despite this current reality, much of the moral concern over deepfakes is grounded in their potential to easily spread misinformation. Criticism around deepfakes in recent years has been mainly surrounding their potential for manipulating the public to achieve political ends. It is becoming increasingly easy to spread a fake video depicting a politician who is clearly incompetent or spreading a questionable message, which might detract from their base. On a more local level, deepfakes could be used to discredit individuals. One could imagine a world in which deepfakes are used to frame someone in order to damage their reputation, or even to suggest they have committed a crime. Video and photo evidence is commonly used in our civil and criminal justice system, and the ability to manipulate videos or images of a person, undetected, arguably poses a grave danger to a justice system which relies on our sense of sight and observation to establish objective fact. Perhaps even worse than framing the innocent could be failing to convict the guilty. In fact, a recent study in the journal Crime Science found that deepfakes pose a serious crime threat when it comes to audio and video impersonation and blackmail. What if a deepfake is used to replace a bad actor with a person who does not exist? Or gives plausible deniability to someone who claims that a video or image of them has been altered?

Deepfakes are also inherently dishonest. Two of the most popular social media networks, Instagram and TikTok, inherently rely upon visual media which could be subject to alteration by self-imposed deepfakes. Even if a person’s likeness is being manipulated with their consent and also could have positive consequences, it still might be considered wrong due to the dishonest nature of its content. Instagram in particular has been increasingly flooded with photoshopped images, as there is an entire app market that exists solely for editing photos of oneself, usually to appear more attractive. The morality of editing one’s photos has been hotly contested amongst users and between feminists. Deepfakes only stand to increase the amount of media that is self-edited and the moral debates that come along with putting altered media of oneself on the internet.

Proponents of deepfakes argue that their positive potential far outweighs the negative. Deepfake technology has been used to spark engagement with the arts and culture, and even to bring historical figures back to life, both for educational and entertainment purposes. Deepfakes also hold the potential to integrate AI into our lives in a more humanizing and personal manner. Others, who are aware of the possible negative consequences of deepfakes, argue that the development and research of this technology should not be impeded, as the advancement of the technology can also contribute to research methods of spotting it. And there is some evidence backing up this argument, as the development of deepfake progresses, so do the methods for detecting it. It is not the moral responsibility of those researching deepfake technology to stop, but rather the role of policymakers to ensure the types of harmful consequences mentioned above do not wreak havoc on the public. At the same time, proponents such as David Greene, of the Electronic Frontier Foundation, argue that too stringent limits on deepfake research and technology will “implicate the First Amendment.”

Perhaps then it is not the government nor deepfake creators who are responsible for their harmful consequences, but rather the platforms which make these consequences possible. Proponents might argue that the power of deepfakes is not necessarily from their ability to deceive one individual, but rather the media platforms on which they are allowed to spread. In an interview with Digital Trends, the creator of Ctrl Shift Face (a popular deepfake YouTube channel), contended that “If there ever will be a harmful deepfake, Facebook is the place where it will spread.” While this shift in responsibility might be appealing, detractors might ask how practical it truly is. Even websites that have tried to regulate deepfakes are having trouble doing so. Popular pornography website, PornHub, has banned deepfake videos, but still cannot fully regulate them. In 2019, a deepfake video of Ariana Grande was watched 9 million times before it was taken down.

In December, the first federal regulation pertaining to deepfakes passed through the House, the Senate, and was signed into law by President Trump. While increased government intervention to prevent the negative consequences of deepfakes will be celebrated by some, researchers and creators will undoubtedly push back on these efforts. Deepfakes are certainly not going anywhere for now, but it remains to be seen if the potentially responsible actors will work to ensure their consequences remain net-positive.

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In Search of an AI Research Code of Conduct

image of divided brain; fluid on one side, curcuitry on the other

The evolution of an entire industry devoted to artificial intelligence has presented a need to develop ethical codes of conduct. Ethical concerns about privacy, transparency, and the political and social effects of AI abound. But a recent study from the University of Oxford suggests that borrowing from other fields like medical ethics to refine an AI code of conduct is problematic. The development of an AI ethics means that we must be prepared to address and predict ethical problems and concerns that are entirely new, and this makes it a significant ethical project. How we should proceed in this field is itself a dilemma. Should we proceed in a top-down principled approach or a bottom up experimental approach?

AI ethics can concern itself with everything from the development of intelligent robots to machine learning, predictive analytics, and the algorithms behind social media websites. This is why it is such an expansive area with some focusing on the ethics of how we should treat artificial intelligence, others focusing on how we can protect privacy, or some on how the AI behind social media platforms and AI capable of generating and distributing ‘fake news’ can influence the political process. In response many have focused on generating a particular set of principles to guide AI researchers; in many cases borrowing from codes governing other fields, like medical ethics.

The four core principles of medical ethics are respect for patient autonomy, beneficence, non-maleficence, and justice. Essentially these principles hold that one should act in the best interests of a patient while avoiding harms and ensuring fair distribution of medical services. But the recent Oxford study by Brent Mittelstadt argues that the analogical reasoning relating the medical field to the AI field is flawed. There are significant differences between medicine and AI research which makes these principles not helpful or irrelevant.

The field of medicine is more centrally focused on promoting health and has a long history of focusing on the fiduciary duties of those in the profession towards patients. Alternatively, AI research is less homogeneous, with different researchers in both the public and private sector working on different goals and who have duties to different bodies. AI developers, for instance, do not commit to public service in the same way that a doctor does, as they may only responsible to shareholders. As the study notes, “The fundamental aims of developers, users, and affected parties do not necessarily align.”

In her book Towards a Code of Ethics for Artificial Intelligence Paula Boddington highlights some of the challenges of establishing a code of ethics for the field. For instance, those working with AI are not required to receive accreditation from any professional body. In fact,

“some self-taught, technically competent person, or a few members of a small scale start up, could be sitting in their mother’s basement right now dreaming up all sorts of powerful AI…Combatting any ethical problems with such ‘wild’ AI is one of the major challenges.”

Additionally, there are mixed attitudes towards AI and its future potential. Boddington notes a divide in opinion: the West is more alarmist as compared to nations like Japan and Korea which are more likely to be open and accepting.

Given these challenges, some have questioned whether an abstract ethical code is the best response. High-level principles which are abstract enough to cover the entire field will be too vague to be action-guiding, and because of the various different fields and interests, oversight will be difficult. According to Edd Gent,

“AI systems are…created by large interdisciplinary teams in multiple stages of development and deployment, which makes tracking the ethical implications of an individual’s decisions almost impossible, hampering our ability to create standards to guide those choices.”

The situation is not that different from work done in the sciences. Philosopher of science Heather Douglas has argued, for instance, that while ethical codes and ethical review boards can be helpful, constant oversight is impractical, and that only scientists can fully appreciate the potential implications of their work. The same could be true of AI researchers. A code of principles of ethics will not replace ethical decision-making; in fact, such codes can be morally problematic. As Boddington argues, “The very idea of parceling ethics into a formal ‘code’ can be dangerous.” This is because many ethical problems are going to be new and unique so ethical choice cannot be a matter of mere compliance. Following ethical codes can lead to complacency as one seeks to check certain boxes and avoid certain penalties without taking the time to critically examine what may be new and unprecedented ethical issues.

What this suggests is that any code of ethics can only be suggestive; they offer abstract principles that can guide AI researchers, but ultimately the researchers themselves will have to make individual ethical judgments. Thus, part of the moral project of developing an AI ethics is going to be the development of good moral judgment by those in the field. Philosopher John Dewey noted this relationship between principles and individual judgment, arguing:

“Principles exist as hypotheses with which to experiment…There is a long record of past experimentation in conduct, and there are cumulative, verifications which give many principles a well earned prestige…But social situations alter; and it is also foolish not to observe how old principles actually work under new conditions, and not to modify them so that they will be more effectual instruments in judging new cases.”

This may mirror the thinking of Brent Mittelstadt who argues for a bottom-up approach to AI ethics that focuses on sub-fields developing ethical principles as a response to resolving challenging novel cases. Boddington, for instance, notes the importance of equipping researchers and professionals with the ethical skills to make nuanced decisions in context; they must be able to make contextualized interpretations of rules, and to judge when rules are no longer appropriate. Still, such an approach has its challenges as researchers must be aware of the ethical implications of their work, and there still needs to be some oversight.

Part of the solution to this is public input. We as a public need to make sure that corporations, researchers, and governments are aware of the public’s ethical concerns. Boddington recommends that in such input there be a diversity of opinion, thinking style, and experience. This includes not only those who may be affected by AI, but also professional experts outside of the AI field like lawyers, economists, social scientists, and even those who have no interest in the world of AI in order maintain an outside perspective.

Codes of ethics in AI research will continue to develop. The dilemma we face as a society is what such a code should mean, particularly whether it will be institutionalized and enforced or not. If we adopt a bottom up approach, then such codes will likely be only there for guidance or will require the adoption of multiple codes for different areas. If a more principled top-down approach is adopted, then there will be additional challenges of dealing with the novel and with oversight. Either way, the public will have a role to play to ensure that its concerns are being heard.

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Racist, Sexist Robots: Prejudice in AI

Black and white photograph of two robots with computer displays

The stereotype of robots and artificial intelligence in science fiction is largely of a hyper-rational being, unafflicted by the emotions and social infirmities like biases and prejudices that impair us weak humans. However, there is reason to revise this picture. The more progress we make with AI the more a particular problem comes to the fore: the algorithms keep reflecting parts of our worst selves back to us.

In 2017, research showed compelling evidence that AI picks up deeply ingrained racial- and gender-based prejudices. Current machine learning techniques rely on algorithms interacting with people in order to better predict correct responses over time. Because of the dependence on interacting with humans for standards of correctness, the algorithms cannot detect when bias informs a correct response or when the human is engaging in a non-prejudicial way. Thus, the best working AI algorithms pick up the racist and sexist underpinnings of our society. Some examples: the words “female” and “woman” were more closely associated with arts and humanities occupations and with the home, while “male” and “man” were closer to maths and engineering professions. Europeans were associated with pleasantness and excellence.

In order to prevent discrimination in housing, credit, and employment, Facebook has recently been forced to agree to an overhaul of its ad-targeting algorithms. The functions that determined how to target audiences for ads relating to these areas turned out to be racially discriminatory, not by design – the designers of the algorithms certainly didn’t encode racial prejudices – but because of the way they are implemented. The associations learned by the ad-targeting algorithms led to disparities in the advertising of major life resources. It is not enough to program a “neutral” machine learning algorithm (i.e., one that doesn’t begin with biases). As Facebook learned, the AI must have anti-discrimination parameters built in as well. Characterizing just what this amounts to will be an ongoing conversation. For now, the ad-targeting algorithms cannot take age, zip code, or gender into consideration, as well as legally protected categories.

The issue facing AI is similar to the “wrong kind of reasons” problem in philosophy of action. The AI can’t tell a systemic bias of humans from a reasoned consensus: both make us converge on an answer and support the algorithm to select what we may converge on. It is difficult to say what, in principle, the difference is between the systemic bias and a reasoned consensus is. It is difficult, in other words, to give the machine learning instrument parameters to tell when there is the “right kind of reason” supporting a response and the “wrong kind of reason” supporting the response.

In philosophy of action, the difficulty of drawing this distinction is illustrated by a case where, for instance, you are offered $50,000 to (sincerely) believe that grass is red. You have a reason to believe, but intuitively this is the wrong kind of reason. Similarly, we could imagine a case where you will be punished unless you (sincerely) desire to eat glass. The offer of money doesn’t show that “grass is red” is true, similarly the threat doesn’t show that eating glass is choice-worthy. But each somehow promote the belief or desire. For the AI, a racist or sexist bias leads to a reliable response in the way that the offer and threat promote a behavior – it is disconnected from a “good” response, but it’s the answer to go with.

For International Women’s Day, Jeanette Winterson suggested that artificial intelligence may have a significantly detrimental effect on women. Women make up 18% of computer science graduates and thus are left out of the design and directing of this new horizon of human development. This exclusion can exacerbate the prejudices that can be inherent in the design of these crucial algorithms that will become more critical to more arenas of life.

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The Artificial Intelligence of Google’s AlphaGo

Last week, Google’s AlphaGo program beat Ke Jie, the Go world champion. The victory is a significant one, due to the special difficulties of developing an algorithm that can tackle the ancient Chinese game. It differs significantly from the feat of DeepBlue, the computer that beat then-chess world champion Garry Kasparov in 1997, largely by brute force calculations of the possible moves on the 8×8 board. The possible moves in Go far eclipse those of chess, and for decades most researchers didn’t consider it possible for a computer to defeat a champion-level Go player, because designing a computer with such complexity would amount to such great leaps towards creative intuition on the computer’s part.

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