John Broome; V*—Fairness, Proceedings of the Aristotelian Society, Volume 91, Issue 1, 1 June 1991, Pages 87–102, https://doi.org/10.1093/aristotelian/91.1.87.

There are many objections to statistical discrimination in general and racial profiling in particular. One objection appeals to the idea that people have a right to be treated as individuals. Statistical discrimination violates this right because, presumably, it involves treating people simply on the basis of statistical facts about groups to which they belong while ignoring non-statistical evidence about them. While there is something to this objection—there are objectionable ways of treating others that seem aptly described as failing to treat (...) them as individuals—it needs to be articulated carefully. First, most people accept that many forms of statistical discrimination are morally unproblematic, let alone morally justified all things considered. Second, even treating people on the basis of putative non-statistical evidence relies on generalizations. Once we construe treating someone as an individual in a way that respects this fact, it becomes apparent: (1) that statistical discrimination is compatible with treating people as individuals, and (2) that one may fail to treat people as individuals even without engaging in statistical discrimination. Finally, there are situations involving the expression of messages of inclusion where we think it is good, morally speaking, that we are not treated as individuals. (shrink)

Journal of Political Philosophy, Volume 30, Issue 2, Page 209-229, June 2022.

We have benefited from conversations with Archon Fung, Brian Jacob, Todd Pittinsky, Peter Schuck, Ani Satz, Andrew Williams, and students in a joint class on statistics and ethics at the John F. Kennedy School of Government in October 2002. We are also grateful to our audience at the conference “The Priority of Practice,” organized by Jonathan Wolff at University College London in September 2003, and to Arthur Applbaum, Miriam Avins, Frances Kamm, Simon Keller, Frederick Schauer, Alan Wertheimer, and the Editors (...) of Phi- losophy & Public Affairs for insightful comments. We have benefited from prepublication reading of Schauer’s work on profiling, Profiles, Probabilities, and Stereotypes (Cambridge, Mass: Harvard University Press, 2003). We thank Avedis Koutoujian for research assistance. (shrink)

We argue that some algorithms are value-laden, and that two or more persons who accept different value-judgments may have a rational reason to design such algorithms differently. We exemplify our claim by discussing a set of algorithms used in medical image analysis: In these algorithms it is often necessary to set certain thresholds for whether e.g. a cell should count as diseased or not, and the chosen threshold will partly depend on the software designer’s preference between avoiding false positives and (...) false negatives. This preference ultimately depends on a number of value-judgments. In the last section of the paper we discuss some general principles for dealing with ethical issues in algorithm-design. (shrink)

Mainstream moral theories deal with situations in which the outcome of each possible action is well-determined and knowable. In order to make ethics relevant for problems of risk and uncertainty, moral theories have to be extended so that they cover actions whose outcomes are not determinable beforehand. One approach to this extension problem is to develop methods for appraising probabilistic combinations of outcomes. This approach is investigated and shown not to solve the problem. An alternative approach is then developed. Its (...) starting-point is that everyone has a prima facie moral right not to be exposed to risk. However, this right can be overridden if the risk-exposure is part of an equitable system for risk-taking that works to the advantage of the individual risk-exposed person. (shrink)

The law views with suspicion statistical evidence, even evidence that is probabilistically on a par with direct, individual evidence that the law is in no way suspicious of. But it has proved remarkably hard to either justify this suspicion, or to debunk it. In this paper, we connect the discussion of statistical evidence to broader epistemological discussions of similar phenomena. We highlight Sensitivity – the requirement that a belief be counterfactually sensitive to the truth in a specific way – as (...) a way of epistemically explaining the legal suspicion towards statistical evidence. Still, we do not think of this as a satisfactory vindication of the reluctance to rely on statistical evidence. Knowledge – and Sensitivity, and indeed epistemology in general – are of little, if any, legal value. Instead, we tell an incentive-based story vindicating the suspicion towards statistical evidence. We conclude by showing that the epistemological story and the incentive-based story are closely and interestingly related, and by offering initial thoughts about the role of statistical evidence in morality. (shrink)