Deborah Hellman

When do laws and policies that do not explicitly treat people differently on the basis of legally protected traits like race and sex nonetheless constitute disparate treatment on these bases? According to U.S. constitutional law, they do so when “facially neutral” laws are both enacted for impermissible reasons and also produce a discriminatory effect. To date, the first element of this claim – impermissible intention – has attracted significant attention. However, its second element – discriminatory effect – has been largely ignored. Yet it is critical that we better understand what discriminatory effect requires, as competing tests animate debates in Circuit court cases and the issue has recently been flagged by Justice Alito. This Article takes up the task. It explores the normative disagreement that underlies the controversy regarding how to assess whether discriminatory effect is present and diagnoses the genuine moral conflict that any test for discriminatory harm must navigate.

This article argues that the fact that an action will compound a prior injustice counts as a reason against doing the action. I call this reason The Anti-Compounding Injustice principle or aci. Compounding injustice and the aci principle are likely to be relevant when analyzing the moral issues raised by “big data” and its combination with the computational power of machine learning and artificial intelligence. Past injustice can infect the data used in algorithmic decisions in two distinct ways. Sometimes prior injustice undermines the accuracy of the data itself. In these contexts, improving accuracy will also help to avoid compounding injustice. Other times, past injustice produces real-world differences among people with regard to skills, health, wealth, and other traits that employers, lenders, and others seek to measure. When decisions are based on accurate data that itself results from prior injustice, these decisions can also compound injustice. This second dynamic has received less attention than the first but is especially important because improving the accuracy of data will not mitigate this unfairness.

This article examines the complaint that arbitrary algorithmic decisions wrong those whom they affect. It makes three contributions. First, it provides an analysis of what arbitrariness means in this context. Second, it argues that arbitrariness is not of moral concern except when special circumstances apply. However, when the same algorithm or different algorithms based on the same data are used in multiple contexts, a person may be arbitrarily excluded from a broad range of opportunities. The third contribution is to explain why this systemic exclusion is of moral concern and to offer a solution to address it.

Willful blindness is not an appropriate substitute for knowledge in crimes that require a mens rea of knowledge because an actor who contrives his own ignorance is only sometimes as culpable as a knowing actor. This paper begins with the assumption that the classic willfully blind actor—the drug courier—is culpable. If so, any plausible account of willful blindness must provide criteria that find this actor culpable. This paper then offers two limiting cases: a criminal defense lawyer defending a client he suspects of perjury and a pain doctor who suspects his patient may be lying about her pain. The paper argues that each of these actors is justified in cultivating ignorance about his client’s or patient’s truthfulness. If this is right, then a good theory of willful blindness must distinguish these cases. The article argues that neither Husak & Callender’s motivation-based account of willful blindness nor the recklessness account is able to do so. The paper proposes the following alternative: contrived ignorance constitutes culpable blindness when the decision to remain blind or to cultivate blindness is not itself justified. This Justification approach meshes with our intuitions about willfully blind drug couriers as well as willfully blind lawyers and doctors.

Clinical research employing the randomized clinical trial has, traditionally, been understood to pose an ethical dilemma. On the one hand, each patient ought to get the treatment that best meets her needs, as judged by the patient in consultation with her doctor. On the other hand, the method most helpful to advancing our understanding about what treatments are indeed best able to meet patient needs is the randomized trial, which necessitates that each patient's care is decided not by physician judgment or patient choice but instead by random assignment. The tension can be described as a conflict between the interests of individual patients who are sick today, and the interests of the group of people who will become sick in the future and would benefit from advances in medical understanding. How one ought to balance these important and often competing interests is an important ethical question that resists easy resolution.