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The Real Threat of AI

digitized image of human raising fist in resistance

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

The notion of AI performing creative tasks is significant.

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

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

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

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

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

However, let us set those concerns aside.

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

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

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

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

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

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

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

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

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

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

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

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

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The Ethics of AI Behavior Manipulation

photograph of server room

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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Can Machines Be Morally Responsible?

photograph of robot in front of chalkboard littered with question marks

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

photograph of middle school boy using computer

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

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

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

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

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

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

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

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

image of screen covered in binary code

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

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

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

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

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

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

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

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

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

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

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Who Is Accountable for Inductive Risk in AI?

computer image of programming decision trees

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

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

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

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

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

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

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

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

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

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

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Resurrection Through Chatbot?

cartoon image of an occult seance

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

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

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

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

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

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

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

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

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

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

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

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

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The Ethics of Policing Algorithms

photograph of silhouettes watching surveillance monitors

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

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

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

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

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

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

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

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Will the Real Anthony Bourdain Please Stand Up?

headshot of Anthony Bourdain

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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Ethical Considerations of Deepfakes

computer image of two identical face scans

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

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

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

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

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

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

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

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

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

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

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In Search of an AI Research Code of Conduct

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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Racist, Sexist Robots: Prejudice in AI

Black and white photograph of two robots with computer displays

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

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

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

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

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

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

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The Artificial Intelligence of Google’s AlphaGo

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

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