A number of philosophers argue that law enforcement officers may have good reasons to racially profile suspects under certain conditions. Their conclusions rest on a claim of epistemic rationality: if members of some races are at an increased risk of criminality, then it may be rational for law enforcement officers to subject them to increased scrutiny. In this paper I contest the epistemic rationality of racial profiling by appealing to recent work in criminology and the sociology of race and crime. (...) I argue that recent studies indicate that race is a comparatively poor baseline for judging criminality. Law enforcement officers are therefore not making a cognitive error by ignoring race to focus on other correlates of crime but are keeping up with our best social science. (shrink)

Some, but not all, of the mistakes a person makes when acting in apparently necessary self-defense are reasonable: we take them not to violate the rights of the apparent aggressor. I argue that this is explained by duties grounded in agents' entitlements to a fair distribution of the risk of suffering unjust harm. I suggest that the content of these duties is filled in by a social signaling norm, and offer some moral constraints on the form such a norm can (...) take. (shrink)

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. (shrink)