Many seem to think that AI-induced responsibility gaps are morally bad and therefore ought to be avoided. We argue, by contrast, that there is at least a pro tanto reason to welcome responsibility gaps. The central reason is that it can be bad for people to be responsible for wrongdoing. This, we argue, gives us one reason to prefer automated decision-making over human decision-making, especially in contexts where the risks of wrongdoing are high. While we are not the first to (...) suggest that responsibility gaps should sometimes be welcomed, our argument is novel. Others have argued that responsibility gaps should sometimes be welcomed because they can reduce or eliminate the psychological burdens caused by tragic moral choice-situations. By contrast, our argument explains why responsibility gaps should sometimes be welcomed even in the absence of tragic moral choice-situations, and even in the absence of psychological burdens. (shrink)

The stakes associated with an algorithmic decision are often said to play a role in determining whether the decision engenders a right to an explanation. More specifically, “high stakes” decisions are often said to engender such a right to explanation whereas “low stakes” or “non-high” stakes decisions do not. While the overall gist of these ideas is clear enough, the details are lacking. In this paper, we aim to provide these details through a detailed investigation of what we will call (...) the “Simple Stakes Thesis”. The Simple Stakes Thesis, as it will turn out, is too simple. For even if the stakes associated with a specific one-off decision are low—and hence does not engender a right to an explanation—such decisions may nevertheless form part of a high stakes pattern or aggregate of decisions. In such cases, we argue, even a low stakes decision may engender a right to explanation. Not only does this show that the right to explanation is more demanding than so far recognized. It also shows that the stakes thesis is significantly harder to apply in practice. (shrink)

According to a popular view, privacy is a function of people not knowing or rationally believing some fact about you. But intuitively it seems possible for a perpetrator to violate your right to privacy without learning any facts about you. For example, it seems plausible to say that the US National Security Agency’s PRISM program violated, or could have violated, the privacy rights of the people whose information was collected, despite the fact that the NSA, for the most part, merely (...) collected information without examining it, and thus, for the most part, did not acquire knowledge about specific people. This pair of judgements creates the challenge of aligning one of the most popular accounts of what privacy is with a suitable account of the right to privacy. Call this the alignment challenge. -/- In this paper, I criticize two recent attempts to answer the alignment challenge: one based on risk, proposed by Björn Lundgren, and one based on modal robustness, proposed by Carissa Véliz. I then offer my preferred response, which I call the inquiry account. According to this account, the right to privacy protects against certain forms of inquiry. This account explains why there is a gap between what privacy is a what the right to privacy protects people against. Moreover, it explains why it is possible to violate someone’s right to privacy without obtaining knowledge. (shrink)

Philosophy &Public Affairs, Volume 52, Issue 1, Page 69-100, Winter 2024.

In this paper, we defend what we call the ‘Hybrid View’ of privacy. According to this view, an individual has privacy if, and only if, no one else forms an epistemically warranted belief about the individual’s personal matters, nor perceives them. We contrast the Hybrid View with what seems to be the most common view of what it means to access someone’s personal matters, namely the Belief-Based View. We offer a range of examples that demonstrate why the Hybrid View is (...) more plausible than the Belief-Based View. Finally, we show how the Hybrid View generates a more plausible fit between the concept of privacy, and the concept of a (morally objectionable) violation of privacy. (shrink)

Some say that we should respect the privacy of dead people. In this article, I take this idea for granted and use it to motivate the stronger claim that we sometimes ought to respect the privacy of our past selves.

Artificial Intelligence (AI) often outperforms human doctors in terms of decisional speed. For some diseases, the expected benefit of a fast but less accurate decision exceeds the benefit of a slow but more accurate one. In such cases, we argue, it is often justified to rely on a medical AI to maximise decision speed – even if the AI is less accurate than human doctors.

Many believe that we ought to treat people as individuals and that this form of treatment is in some sense incompatible with treating people as members of groups. Yet, the relation between these two kinds of treatments is elusive. In this paper, we develop a novel account of the normative requirement to treat people as individuals. According to this account, treating people as individuals requires treating people as agents in the appropriate capacity. We call this the Agency Attunement Account. This (...) view has the surprising implication that it’s not only possible to fail in treating people as individuals but also possible to fail in treating people as members of groups, on the presumption that people’s agency is sometimes nested in, and exercised via, agential groups. (shrink)

Relational egalitarianism is a theory of justice according to which justice requires that people relate as equals. According to some relational egalitarians, X and Y relate as equals if, and only if, they (1) regard each other as equals; and (2) treat each other as equals. In this paper, we argue that relational egalitarians must give up 1.

By now a consensus has emerged that people, when subjected to high-stakes decisions through automated decision systems, have a moral right to have these decisions explained to them. However, furnishing such explanations can be costly. So the right to an explanation creates what we call the cost problem: providing subjects of automated decisions with appropriate explanations of the grounds of these decisions can be costly for the companies and organisations that use these automated decision systems. In this paper, we explore (...) whether large language models could prove significant in overcoming the cost problem. We provide an initial case for believing that they can but only with serious ethical costs. (shrink)

What makes discrimination morally bad? In this paper, we discuss the putative badness of a case of consensual discrimination to show that prominent accounts of the badness of discrimination—appealing, inter alia, to harm, disrespect and inequality—fail to provide a satisfactory answer to this question. In view of this, we present a more promising account.

This paper concerns the double standard debate in the ethics of AI literature. This debate essentially revolves around the question of whether we should subject AI systems to different normative standards than humans. So far, the debate has centered around the desideratum of transparency. That is, the debate has focused on whether AI systems must be more transparent than humans in their decision-making processes in order for it to be morally permissible to use such systems. Some have argued that the (...) same standards of transparency should be applied across the board, for AI systems and humans alike. Others have argued that we should hold AI systems to higher standards than humans in terms of transparency. In this paper, we first point out that there are structurally similar double standard debates to be had about other desiderata besides transparency, such as predictive accuracy. Second, we argue that when we focus on predictive accuracy, there are at least two reasons for holding AI systems to a lower standard than humans. (shrink)

Often, when we share information about ourselves, we contribute to people learning personal things about others. This may happen because what we share about ourselves can be used to infer personal information about others. Such dependencies have become known as privacy dependencies in the literature. It is sometimes claimed that the scope of the right to privacy should be expanded in light of such dependencies. For example, some have argued that inferring information about others can violate their right to privacy. (...) Others have argued that sharing personal information about yourself that license such inferences can by itself violate the right to privacy. In this paper, we argue that the latter view should be rejected. (shrink)

According to the relational egalitarian theory of justice, justice requires that people relate as equals. To relate as equals, many relational egalitarians argue, people must (i) regard each other as equals, and (ii) treat each other as equals. In this paper, we argue that, under conditions of background injustice, such relational egalitarians should endorse affirmative action in the ways in which (dis)esteem is attributed to people as part of the regard-requirement for relating as equals.

If I decide to disclose information about myself, this act may undermine other people’s ability to conceal information about them. Such dependencies are called privacy dependencies in the literature. Some say that privacy dependencies generate moral duties to avoid sharing information about oneself. If true, we argue, then it is sometimes justified for others to impose harm on the person sharing information to prevent them from doing so. In this paper, we first show how such conclusions arise. Next, we show (...) that the existence of such a dependency between the moral significance you are inclined to attribute to privacy dependencies and judgments about permissible self-defense puts pressure on at least some ways of spelling out the idea that privacy dependencies ought to constrain our data-sharing conduct. (shrink)