'Statistical Discrimination' Annabelle Lever Université de Genève Annabelle.lever@unige.ch Forthcoming in The Philosophers Magazine, (Feb. 2016) Racial discrimination uses race as grounds to discriminate in the treatment owed to others; sexual discrimination uses people's sexual features as grounds for determining how they should be treated compared to others. Analogously, statistical discrimination treats statistical inferences about the groups to which individuals belong as grounds for discriminating amongst them in thought, word and deed. Examples of statistical discrimination include the employer who won't hire women of childbearing age, because they are likely to take maternity leave at some point in their careers; or insurers who wish to charge more to young drivers than to more experienced ones, because they are more likely to have accidents than the latter – or to favour women over men in the cost of premiums for similar reasons. Finally, a famous – infamous – example of statistical discrimination is racial profiling for police purposes, where statistical evidence of differential propensities to crime are used to justify preventive police measures, such as 'stop and search', which mainly target young black men, and other racialized minorities. The philosophical appeal of the concept of statistical discrimination is fairly easy to see: it draws our attention to the way that probabilistic claims figure, or might figure, in the distribution of social costs and benefits such as jobs, security, insurance. It therefore gives us a way to test our moral intuitions across different cases, enables us to see how far our 2 moral judgements can be generalised, and whether they have been affected by particularly emotive examples, or by the particular features of a real or hypothetical case. Thus, Kasper Lippert-Rasmussen has argued there is nothing obviously wrong with using statistical evidence to determine our judgements when finer-grained evidence is unavailable or too difficult to obtain. In so far as there is something morally wrong with particular cases of statistical discrimination, he argues, the moral wrong is not in the use of statistics but some other feature(s) of the differential treatment, or of the reasons for it. For example, from a feminist perspective, the problem with the employer who discriminates against fertile women is not that s/he is using statistical generalisations in order to decide who to hire, nor that s/he is failing adequately to acknowledge that some women of childbearing age cannot have, or do not want to have, children. The objection, rather, is to a situation where women can be legally penalised because of their fertility, although women generally get pregnant as a result of sex with men. Likewise, we might think it unfair to use a form of policing, such as racial profiling, whose burdens are significant and fall overwhelmingly on disadvantaged social groups, when the majority of people, no matter their age, sex, race or religion, do not engage in criminal activity and when there are other ways -random searches, universal searches, social policies-to deter and prevent crime. The insurance cases seem to confirm the general point that statistical discrimination in itself is not particularly problematic. Private insurance is not possible if companies cannot protect themselves against bad risks by charging higher premiums, or by refusing to insure particularly difficult cases altogether. Hence, there seems nothing inherently objectionable in requiring inexperienced drivers to pay more than drivers with a record of years of safe driving behind them, so long as the disparities in premiums do not make it all but impossible 3 for most youngsters to drive, and therefore to acquire the experience that might reduce their premiums in future. If we are troubled by the idea of men paying more than women, then, this may reflect the sense that a pure private-insurance approach to car insurance fails adequately to reflect the socially important dimensions of driving in our societies. For example, if men do more driving than women, and more driving in difficult circumstances, it seems unfair to penalise them financially as compared to women by making them pay higher insurance premiums. But is statistical discrimination as innocuous as it seems, and what, really, can we conclude about it from cases like these? After all, if statistical generalisations now figure in the justification of inequalities where 'common sense', religion, personal choice or employer preference might once have been sufficient, should we not ask what gives statistical generalisations this justificatory power, and how well-founded that power is epistemologically, morally and politically? Statistics, if accurate and useful, have to be constructed with care and difficulty from myriad sources which may not yet be suitable for statistical comparison and manipulation. Many of us lack the ability to understand (or accurately to report) quite basic statistical claims, let alone to assess more complex questions about the adequacy of different techniques for generating and interpreting them. In short, there is a political economy and sociology of evidence that we need to consider when deciding whether the use of statistical evidence justifies treating otherwise similar people differently. An explicitly critical and socialconstructionist perspective on racial profiling, the hiring and promotion of employees or the treatment of risk is often a necessary philosophical supplement to more analytic studies. Analogously, we may have to supplement analytic approaches to statistical justification with 4 more critical perspectives, in order to evaluate the role of statistical generalisations in contemporary practices of justification. Suggestions for Further Reading: Kasper Lippert-Rasmussen, Born Free and Equal? A Philosophical Inquiry Into the Nature of Discrimination (Oxford University Press, 2014) Sally Haslanger, Resisting Reality: Social Construction and Social Critique, (Oxford University Press, 2012) R. J. Sampson and J. D. Raudenbush, 'Social Anatomy of Racial and Ethnic Disparities in Violence', American Journal of Public Health, 95.2. (2005).