This article analyses the moral status of racial profiling from a consequentialist perspective and argues that, contrary to what proponents of racial profiling might assume, there is a prima facie case against racial profiling on consequentialist grounds. To do so it establishes general definitions of police practices and profiling, sketches out the costs and benefits involved in racial profiling in particular and presents three challenges. The foundation challenge suggests that the shifting of burdens onto marginalized minorities may, even when profiling (...) itself is justified, serve to prolong unjustified police practices. The valuation challenge argues that although both costs and benefits are difficult to establish, the benefits of racial profiling are afflicted with greater uncertainty than the costs, and must be comparatively discounted. Finally, the application challenge argues that using racial profiling in practice will be complicated by both cognitive and psychological biases, which together reduce the effectiveness of profiling while still incurring its costs. Jointly, it is concluded, these challenges establish a prima facie case against racial profiling, so that the real challenge consists in helping officers practice the art of the police and not see that which it is useless that they should see. (shrink)

Most social policies cannot be defended without making inductive inferences. For example, consider certain arguments for racial profiling and affirmative action, respectively. They begin with statistics about crime or socioeconomic indicators. Next, there is an inductive step in which the statistic is projected from the past to the future. Finally, there is a normative step in which a policy is proposed as a response in the service of some goal—for example, to reduce crime or to correct socioeconomic imbalances. In comparison (...) to the normative step, the inductive step of a policy defense may seem trivial. We argue that this is not so. Satisfying the demands of the inductive step is difficult, and doing so has important but underappreciated implications for the normative step. In this paper, we provide an account of induction in social contexts and explore its implications for policy. Our account helps to explain which normative principles we ought to accept, and as a result it can explain why it is acceptable to make inferences involving race in some contexts (e.g., in defense of affirmative action) but not in others (e.g., in defense of racial profiling). (shrink)

This article considers what can be learned regarding the ethical acceptability of intrusive interventions intended to halt the spread of infectious disease (‘Infection Control’ measures) from existing ethical discussion of intrusive interventions used to prevent criminal conduct (‘Crime Control’ measures). The main body of the article identifies and briefly describes six objections that have been advanced against Crime Control, and considers how these might apply to Infection Control. The final section then draws out some more general lessons from the foregoing (...) analysis for the ethical acceptability of different kinds of Infection Control. (shrink)

This essay begins with a current case involving racial profiling and DNA testing. The two combine to raise some troubling issues involving the use of each in police investigation. It is argued that racial profiling is unethical and ought to be avoided and that DNA testing on general populations of innocent people is fraught with dangers.

It is often thought that traditional recidivism prediction tools used in criminal sentencing, though biased in many ways, can straightforwardly avoid one particularly pernicious type of bias: direct racial discrimination. They can avoid this by excluding race from the list of variables employed to predict recidivism. A similar approach could be taken to the design of newer, machine learning-based (ML) tools for predicting recidivism: information about race could be withheld from the ML tool during its training phase, ensuring that the (...) resulting predictive model does not use race as an explicit predictor. However, if race is correlated with measured recidivism in the training data, the ML tool may ‘learn’ a perfect proxy for race. If such a proxy is found, the exclusion of race would do nothing to weaken the correlation between risk (mis)classifications and race. Is this a problem? We argue that, on some explanations of the wrongness of discrimination, it is. On these explanations, the use of an ML tool that perfectly proxies race would (likely) be more wrong than the use of a traditional tool that imperfectly proxies race. Indeed, on some views, use of a perfect proxy for race is plausibly as wrong as explicit racial profiling. We end by drawing out four implications of our arguments. (shrink)