Algorithmic discrimination has rapidly become a topic of intense public and academic interest. This article explores three issues raised by algorithmic discrimination: 1) the distinction between direct and indirect discrimination, 2) the notion of disadvantageous treatment, and 3) the moral badness of discriminatory automated decision-making. It argues that some conventional distinctions between direct and indirect discrimination appear not to apply to algorithmic discrimination, that algorithmic discrimination may often be discrimination between groups, as opposed to against groups, and that it is (...) not necessarily the case that morally bad algorithmic discrimination gives us reason to not use automated decision-making. For each of the three issues, the article explores implications for algorithmic discrimination, suggests some alternative answers, and clarifies how we may want to think of discrimination more broadly in light of lessons drawn from the context of algorithmic discrimination. (shrink)

In a recent article in this journal, Isaac Taylor warned against the unconstrained use of algorithms as instruments to determine sentences in criminal cases. More precisely, what he argued is that it is important that the sentencing process serves a condemnatory function, and that the introduction of sentencing algorithms threatens to undermine this function. In this reply to Taylor, it is argued that even though his considerations are interesting as they direct attention to the sentencing process and not merely the (...) output of algorithmic sentencing, there are nevertheless reasons to be skeptical of his argument. More precisely, it is argued, first, that it is far from clear that we must accept the basic premise underlying Taylor’s argument, namely, that the sentencing process should serve a condemnatory function and, second, that even if this premise is true, it does not follow that the condemnatory element of the sentencing process is undermined by introducing algorithms as advisory instruments at sentencing. (shrink)