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Toward an Ethical Theory of Consciousness for AI

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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.

LaMDA, Lemoine, and the Problem with Sentience

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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.

AI Sentience and Moral Risk

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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.

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.

The Real Threat of AI

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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.

The Ethics of AI Behavior Manipulation

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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.

Can Machines Be Morally Responsible?

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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.

AI and Pure Science

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In September 2019, four researchers wrote to the academic publisher Wiley to request that it retract a scientific paper relating to facial recognition technology. The request was made not because the research was wrong or reflected bad methodology, but rather because of how the technology was likely to be used. The paper discussed the process by which algorithms were trained to detect faces of Uyghur people, a Muslim minority group in China. While researchers believed publishing the paper presented an ethical problem, Wiley defended the article noting that it was about a specific technology, not about the application of that technology. This event raises a number of important questions, but, in particular, it demands that we consider whether there is an ethical boundary between pure science and applied science when it comes to AI development – that is, whether we can so cleanly separate knowledge from use as Wiley suggested.

The 2019 article for the journal WIREs Data Mining and Knowledge Discovery discusses discoveries made by the research term in the work on ethic group facial recognition which included datasets of Chinese Uyghur, Tibetan, and Korean students at Dalian University. In response a number of researchers, believing that it is disturbing that academics tried to build such algorithms, called for the article to be retracted. China has been condemned for its heavy surveillance and mass detention of Uyghurs, and this study and a number of other studies, some scientists claim, are helping to facilitate the development of technology which can make this surveillance and oppression more effective. As Richard Van Noorden reports, there has been a growing push by some scientists to get the scientific community to take a firmer stance against unethical facial-recognition research, not only denouncing controversial uses of technology, but the research foundations of it as well. They call on researchers to avoid working with firms or universities linked to unethical projects.

For its part, Wiley has defended the article, noting “We are aware of the persecution of the Uyghur communities … However, this article is about a specific technology and not an application of that technology.” In other words, Wiley seems to be adopting an ethical position based on the long-held distinction between pure and applied science. This distinction is old, tracing back to the time of Francis Bacon and the 16th century as part of a compromise between the state and scientists. As Robert Proctor reports, “the founders of the first scientific societies promised to ignore moral concerns” in return for funding and for freedom of inquiry in return for science keeping out of political and religious matters. In keeping with Bacon’s urging that we pursue science “for its own sake,” many began to distinguish science as “pure” affair, interested in knowledge and truth by themselves, and applied science which seeks to use engineering to apply science in order to secure various social goods.

In the 20th century the division between pure and applied science was used as a rallying cry for scientific freedom and to avoid “politicizing science.” This took place against a historical backdrop of chemists facilitating great suffering in World War I followed by physicists facilitating much more suffering in World War II. Maintaining the political neutrality of science was thought to make it more objective by ensuring value-freedom. The notion that science requires freedom was touted by well-known physicists like Percy Bridgman who argued,

The challenge to the understanding of nature is a challenge to the utmost capacity in us. In accepting the challenge, man can dare to accept no handicaps. That is the reason that scientific freedom is essential and that artificial limitations of tools or subject matter are unthinkable.

For Bridgman, science just wasn’t science unless it was pure. He explains, “Popular usage lumps under the single world ‘science’ all the technological activities of engineering and industrial development, together with those of so-called ‘pure science.’ It would clarify matters to reserve the word science for ‘pure’ science.” For Bridgman it is society that must decide how to use a discovery rather than the discoverer, and thus it is society’s responsibility to determining how to use pure science rather than the scientists’. As such, Wiley’s argument seems to echo those of Bridgman. There is nothing wrong with developing the technology of facial recognition in and of itself; if China wishes to use that technology to oppress people with it, that’s China’s problem.

On the other hand, many have argued that the supposed distinction between pure and applied science is not ethically sustainable. Indeed, many such arguments were driven by the reaction to the proliferation of science during the war. Janet Kourany, for example, has argued that science and scientists have moral responsibilities because of the harms that science has caused, because science is supported through taxes and consumer spending, and because society is shaped by science. Heather Douglas has argued that scientists shoulder the same moral responsibilities as the rest of us not to engage in reckless or negligent research, and that due to the highly technical nature of the field, it is not reasonable for the rest of society to carry those responsibilities for scientists. While the kind of pure knowledge that Bridgman or Bacon favor has value, these values need to be weighed against other goods like basic human rights, quality of life, and environmental health.

In other words, the distinction between pure and applied science is ethically problematic. As John Dewey argues the distinction is a sham because science is always connected to human concerns. He notes,

It is an incident of human history, and a rather appalling incident, that applied science has been so largely made equivalent for use for private and economic class purposes and privileges. When inquiry is narrowed by such motivation or interest, the consequence is in so far disastrous both to science and to human life.

Perhaps this is why many scientists do not accept Wiley’s argument for refusing retraction; discovery doesn’t happen in a vacuum. It isn’t as if we don’t know why the Chinese government has an interest in this technology. So, at what point does such research become morally reckless given the very likely consequences?

This is also why debate around this case has centered on the issue of informed consent. Critics charge that the Uyghur students who participated in the study were not likely fully informed of its purposes and this could not provide truly informed consent. The fact that informed consent is relevant at all, which Wiley admits, seems to undermine their entire argument as informed consent in this case appears explicitly tied to how the technology will be used. If informed consent is ethically required, this is not a case where we can simply consider pure research with no regard to its application. And these considerations prompted scientists like Yves Moreau to argue that all unethical biometric research should be retracted.

But regardless of how we think about these specifics, this case serves to highlight a much larger issue: given the large number of ethical issues associated with AI and its potential uses we need to dedicate much more of our time and attention to the question of whether some certain forms of research should be considered forbidden knowledge. Do AI scientists and developers have moral responsibilities for their work? Is it more important to develop this research for its own sake or are there other ethical goods that should take precedence?

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.

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

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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. Because AI can take on such biases, some consider how to eliminate such bias. 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.

Who Is Accountable for Inductive Risk in AI?

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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.

The Insufficiency of Black Box AI

image of black box spotlighted and on pedestal

Google and Imperial College London have collaborated in a trial of an AI system for diagnosing breast cancer. Their most recent results have shown that the AI system can outperform the uncorroborated diagnosis of a single trained doctor and perform on par with pairs of trained diagnosticians. The AI system was a deep learning model, meaning that it works by discovering patterns on its own by being trained on a huge database. In this case the database was thousands of mammogram images. Similar systems are used in the context of law enforcement and the justice system. In these cases the learning database is past police records. Despite the promise of this kind of system, there is a problem: there is not a readily available explanation of what pattern the systems are relying on to reach their conclusions. That is, the AI doesn’t provide reasons for its conclusions and so the experts relying on these systems can’t either.

AI systems that do not provide reasons in support of their conclusions are known as “black box” AI. In contrast to these are so-called “explainable AI”. This kind of AI system is under development and likely to be rapidly adopted within the healthcare field. Why is this so? Imagine visiting the doctor and receiving a cancer diagnosis. When you ask the doctor, “Why do you think I have cancer?” they reply only with a blank stare or reply, “I just know.” Would you find this satisfying or reassuring? Probably not, because you have been provided neither reason nor explanation. A diagnosis is not just a conclusion about a patient’s health but also the facts that lead up to that conclusion. There are certain reasons that the doctor might give you that you would reject as reasons that can support a cancer diagnosis.

For example an AI designed at Stanford University system being trained to help diagnosis tuberculosis used non-medical evidence to generate its conclusions. Rather than just taking into account the images of patients’ lungs, the system used information about the type of X-ray scanning device when generating diagnoses. But why is this a problem? If the information about what type of X-ray machine was used has a strong correlation with whether a patient  has tuberculosis shouldn’t that information be put to use? That is, don’t doctors and patients want to maximize the number of correct diagnoses they make? Imagine your doctor telling you, “I am diagnosing you with tuberculosis because I scanned you with Machine X, and people who are scanned by Machine X are more likely to have tuberculosis.” You would not likely find this a satisfying reason for a diagnosis. So if an AI is making diagnoses based on such facts this is a cause for concern.

A similar problem is discussed in philosophy of law when considering whether it is acceptable to convict people on the basis of statistical evidence. The thought experiment used to probe this problem involves a prison yard riot. There are 100 prisoners in the yard, and 99 of them riot by attacking the guard. One of the prisoners did not attack the guard, and was not involved in planning the riot. However there is no way of knowing specifically of each prisoner whether they did, or did not, participate in the riot. All that is known that 99 of the 100 prisoners participated. The question is whether it is acceptable to convict each prisoner based only on the fact that it is 99% likely that they participated in the riot.

Many who have addressed this problem answer in the negative—it is not appropriate to convict an inmate merely on the basis of statistical evidence. (However, David Papineau has recently argued that it is appropriate to convict on the basis of such strong statistical evidence.) One way to understand why it may be inappropriate to convict on the basis of statistical evidence alone, no matter how strong, is to consider the difference between circumstantial and direct evidence. Direct evidence is any evidence which immediately shows that someone committed a crime. For example, if you see Robert punch Willem in the face you have direct evidence that Robert committed battery (i.e., causing harm through touch that was not consented to). If you had instead walked into the room to see Willem holding his face in pain and Robert angrily rubbing his knuckles, you would only have circumstantial evidence that Robert committed battery. You must infer that battery occurred from what you actually witnessed.

Here’s the same point put another way. Given that you saw Robert punch Willem in the face, there is a 100% chance that Robert battered Willem—hence it is direct evidence. On the other hand, given that you saw Willem holding his face in pain and Robert angrily rubbing his knuckles, there is a 0% – 99% chance that Robert battered Willem. The same applies to any prisoner in the yard during the riot: given that they were in the yard during the riot, there is at best a 99% chance that the prisoner attacked the guard. The fact that a prisoner was in the yard at the time of the riot is a single piece of circumstantial evidence in favor of the conclusion that that prisoner attacked the guard. A single piece of circumstantial evidence is not usually taken to be sufficient to convict someone—further corroborating evidence is required.

The same point could be made about diagnoses. Even if 99% of people examined by Machine X have tuberculosis, simply being examined by Machine X is not a sufficient reason to conclude that someone has tuberculosis. Not reasonable doctor would make a diagnosis on such a flimsy basis, and no reasonable court would convict someone on the flimsy basis in the prison yard riot case above. Black box AI algorithms might not be basing diagnoses or decisions about law enforcement on such a flimsy basis. But because this sort of AI system doesn’t provide its reasons, there is no way to tell what makes its accurate conclusions correct, or its inaccurate conclusions incorrect. Any domain like law or medicine where the reasons that underlie a conclusion are crucially important is a domain in which explainable AI is a necessity, and in which black box AI must not be used.

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.

Do Self-Driving Cars Reinforce Socioeconomic Inequality?

A photo of the steering wheel of a Mercedes car.

Recently, Mercedes-Benz stepped into the spotlight after making a bold statement concerning the design of their self-driving cars. The development of autonomous cars has presented a plethora of moral conundrums, one of which is the most ethical way to program cars to respond to emergencies. The dilemma, as presented in a previous article, is one of trying to determine the value of and prioritize human life. Mercedes has declared that they will “program its self-driving cars to save the people inside the car. Every time.” This declaration sheds light on a new issue: is it ethical for car companies to create technology that widens the gap between socioeconomic classes and threatens current societal values?

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Waymo and the Morality of Self-Driving Cars

An image of a Waymo self-driving car.

What was once fiction is becoming a reality. In past decades, sci-fi novels and television have featured self-driving cars; this once-futuristic concept is finally coming to fruition. Will the result mirror the positive outcomes shown in fiction? Self-driving cars are intended to increase safety and efficiency in our society, but what are the moral implications and consequences that could come from such technology?

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Sex in the Age of Sex Robots

Editor’s note: sources linked in this article contain images and videos that some readers may find disturbing.

From self-driving cars to smartphones, artificial intelligence has certainly made its way into our everyday lives. So have questions of robotic ethics. Shows like Westworld and Black Mirror have depicted some of the more controversial and abstract dangers of artificial intelligence. Human sex dolls have always been taboo, but a new development in the technology of these sex dolls, specifically their upgrade to robot status, is especially controversial. The whole notion of buying a robot to have sex with is taboo to say the least, but can these sexual acts become unethical, even if they are perpetrated upon a nonliving thing? Is using a sex robot to simulate rape or pedophilia morally permissible? And to what extent should sex robots be regulated?

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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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Will Robots Ever Deserve Moral and Legal Rights?

Twenty-one years ago (February 10, 1996), Deep Blue, an IBM Supercomputer, defeated Russian Grand Master Gary Kasparov in a game of chess. Kasparov ultimately won the overall match, but a rematch in May of 1997 went to Deep Blue. About six years ago (February 14-15, 2011), another IBM creation named Watson defeated Champions Ken Jennings and Brad Rutter in televised Jeopardy! matches.

The capabilities of computers continue to expand dramatically and surpass human intelligence in certain specific tasks, and it is possible that computing power may develop in the next several decades to match human capacities in areas of emotional intelligence, autonomous decision making and artistic imagination. When machines achieve cognitive capacities that make them resemble humans as thinking, feeling beings, ought we to accord them legal rights? What about moral rights?

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The Tay Experiment: Does AI Require a Moral Compass?

In an age of frequent technological developments and innovation, experimentation with artificial intelligence (AI) has become a much-explored realm for corporations like Microsoft. In March 2016, the company launched an AI chatbot on Twitter named Tay with the handle of TayTweets (@TayandYou). Her Twitter description read: “The official account of Tay, Microsoft’s A.I. fam from the Internet that’s got zero chill! The more you talk the smarter Tay gets.” Tay was designed as an experiment in “conversational understanding” –– the more people communicated with Tay, the smarter she would get, learning to engage Twitter users through “casual and playful conversation.”

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Digital Decisions in the World of Automated Cars

We’re constantly looking towards the future of technology and gaining excitement for every new innovation that makes our lives easier in some way. Our phones, laptops, tablets, and now even our cars are becoming increasingly smarter. Most new cars on the market today are equipped with GPS navigation, cruise control, and even with some intelligent parallel parking programs. Now, self-driving cars have made their way to the forefront of the automotive revolution.

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