Lauritz Munch

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.

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.

Do privacy rights restrict what is permissible to infer about others based on statistical evidence? This paper replies affirmatively by defending the following symmetry: there is not necessarily a morally relevant difference between directly appropriating people’s private information—say, by using an X-ray device on their private safes—and using predictive technologies to infer the same content, at least in cases where the evidence has a roughly similar probative value. This conclusion is of theoretical interest because a comprehensive justification of the thought that statistical inferences can violate privacy rights is lacking in the current literature. Secondly, the conclusion is of practical interest due to the need for moral assessment of emerging predictive algorithms.

Andrei Marmor has recently offered a narrow interpretation of the right to privacy as a right to having a reasonable amount of control over one's self‐presentation. He claims that the interest people have in preventing others from abusing their personal information to do harm is not directly protected by the right to privacy. This article rejects that claim and defends a view according to which concerns about abuse play a central role in fleshing out the appropriate scope of a general right to privacy.