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)

Mary Kate McGowan’s Just Words offers an interesting account of exercitives. On McGowan’s view, one of the things we do with words is change what’s permitted, and we do this ubiquitously, without any special authority or specific intention. McGowan’s account of exercitives is meant to identify a mechanism by which ordinary speech is harmful, and which justifies the regulation of such speech. It is here that I part ways. I make three main arguments. First, McGowan’s focus on harm is misguided; (...) that ordinary speech is harmful is harder to support, and in turn does not less to support conclusions about whether it is wrong and warrants regulation, than Just Words suggests. Second, if the speech is harmful, McGowan’s account of exercitives is ill-suited to explain why; the relevant instances of ordinary speech seem to express speakers’ views about moral norms, not change social norms. Third, McGowan’s argument for why ordinary speech should be regulated because it is a form of discrimination presupposes a great deal that is controversial about what makes discrimination wrong and legally actionable, and even if it is right it may license the regulation of more speech than many, including McGowan, would deem acceptable. (shrink)

The increasing implementation of and reliance on machine-learning (ML) algorithms to perform tasks, deliver services and make decisions in health and healthcare have made the need for fairness in ML, and more specifically in healthcare ML algorithms (HMLA), a very important and urgent task. However, while the debate on fairness in the ethics of artificial intelligence (AI) and in HMLA has grown significantly over the last decade, the very concept of fairness as an ethical value has not yet been sufficiently (...) explored. Our paper aims to fill this gap and address the AI ethics principle of fairness from a conceptual standpoint, drawing insights from accounts of fairness elaborated in moral philosophy and using them to conceptualise fairness as an ethical value and to redefine fairness in HMLA accordingly. To achieve our goal, following a first section aimed at clarifying the background, methodology and structure of the paper, in the second section, we provide an overview of the discussion of the AI ethics principle of fairness in HMLA and show that the concept of fairness underlying this debate is framed in purely distributive terms and overlaps with non-discrimination, which is defined in turn as the absence of biases. After showing that this framing is inadequate, in the third section, we pursue an ethical inquiry into the concept of fairness and argue that fairness ought to be conceived of as an ethical value. Following a clarification of the relationship between fairness and non-discrimination, we show that the two do not overlap and that fairness requires much more than just non-discrimination. Moreover, we highlight that fairness not only has a distributive but also a socio-relational dimension. Finally, we pinpoint the constitutive components of fairness. In doing so, we base our arguments on a renewed reflection on the concept of respect, which goes beyond the idea of equal respect to include respect for individual persons. In the fourth section, we analyse the implications of our conceptual redefinition of fairness as an ethical value in the discussion of fairness in HMLA. Here, we claim that fairness requires more than non-discrimination and the absence of biases as well as more than just distribution; it needs to ensure that HMLA respects persons both as persons and as particular individuals. Finally, in the fifth section, we sketch some broader implications and show how our inquiry can contribute to making HMLA and, more generally, AI promote the social good and a fairer society. (shrink)

Fairness is one of the most prominent values in the Ethics and Artificial Intelligence (AI) debate and, specifically, in the discussion on algorithmic decision-making (ADM). However, while the need for fairness in ADM is widely acknowledged, the very concept of fairness has not been sufficiently explored so far. Our paper aims to fill this gap and claims that an ethically informed re-definition of fairness is needed to adequately investigate fairness in ADM. To achieve our goal, after an introductory section aimed (...) at clarifying the aim and structure of the paper, in section “Fairness in algorithmic decision-making” we provide an overview of the state of the art of the discussion on fairness in ADM and show its shortcomings; in section “Fairness as an ethical value”, we pursue an ethical inquiry into the concept of fairness, drawing insights from accounts of fairness developed in moral philosophy, and define fairness as an ethical value. In particular, we argue that fairness is articulated in a distributive and socio-relational dimension; it comprises three main components: fair equality of opportunity, equal right to justification, and fair equality of relationship; these components are grounded in the need to respect persons both as persons and as particular individuals. In section “Fairness in algorithmic decision-making revised”, we analyze the implications of our redefinition of fairness as an ethical value on the discussion of fairness in ADM and show that each component of fairness has profound effects on the criteria that ADM ought to meet. Finally, in section “Concluding remarks”, we sketch some broader implications and conclude. (shrink)

Analysing two reproductive rights claims brought before the High Court of Namibia and the European Court of Human Rights, this article argues that human dignity is not reducible to a recognized warrant to demand a particular set of goods, services, or treatments. Rather, dignity in the contexts in which women experience sterilization abuse would be better characterized as an existential protest against degradation, a protest that takes concrete form in legal demands for equal citizenship. Equality is conceived here as necessitating (...) the elimination of all forms of discrimination against women in public and private life. The dignity as non-discrimination framework developed in the article thus integrates two of the leading interpretations of dignity in contemporary political philosophy – the existentialist approach that attends to the inward cry against degradation and the view of dignity as the equal, public status of democratic citizenship. (shrink)

Whatever else liberalism involves, it involves the idea that it is objectionable, and often wrong, for the state, or anyone else, to intervene, in certain ways, in certain choices. This article aims to evaluate different possible sources of support for this core liberal idea. The result is a pluralistic view. It defends, but also stresses the limits of, some familiar elements: that some illiberal interventions impair valuable activities and that some violate rights against certain kinds of invasion. More speculatively, it (...) points to two further sources of support for liberalism, each of which represents a certain kind of social standing: a self-sovereignty compromised simply by being subject to certain kinds of commands and a relational equality compromised by the condemnation of choices with which one’s group is identified. (shrink)