This book develops a novel approach to distributive justice by building a theory based on a concept of desert. As a work of applied political theory, it presents a simple but powerful theoretical argument and a detailed proposal to eliminate unmerited inequality, poverty, and economic immobility, speaking to the underlying moral principles of both progressives who already support egalitarian measures and also conservatives who have previously rejected egalitarianism on the grounds of individual freedom, personal responsibility, hard work, or economic efficiency. (...) By using an agnostic, flexible, data-driven approach to isolate luck and ultimately measure desert, this proposal makes equal opportunity initiatives both more accurate and effective as it adapts to a changing economy. It grants to each individual the freedom to genuinely choose their place in the distribution. It provides two policy variations that are perfectly economically efficient, and two others that are conditionally so. It straightforwardly aligns outcomes with widely shared, fundamental moral intuitions. Lastly, it demonstrates much of the above by modeling four policy variations using 40 years of survey data from the Panel Study of Income Dynamics. (shrink)

In this paper we steer a course between two views of the value of equality that are usually understood as diametrically opposed to one another: on the one hand, the view that equality has intrinsic value; on the other, the view that equality is a normatively redundant notion. We proceed by analysing the different ways in which the equal possession of certain relevant properties justifies distributive equality. We then present an account of ‘basic equality’ that serves to single out where (...) this equal possession is really doing normative work. Our account departs from some common assumptions about basic equality but is nevertheless plausible in as much as it assigns to that concept a precise and normatively significant role in grounding the non-instrumental value of distributive equality. By showing when, exactly, equality is basic, we are able to explain both what is right and what is wrong in each of the two opposed views of the value of equality. The argument proceeds at a fairly high level of abstraction and is therefore applicable to a number of rival egalitarian theories. (shrink)

In the luck egalitarian literature, one influential formulation of luck egalitarianism does not specify whether equalities that do not reflect people’s equivalent exercises of responsibility are bad with regard to inequality. This equivocation gives rise to two competing versions of luck egalitarianism: asymmetrical and symmetrical luck egalitarianism. According to the former, while inequalities due to luck are unjust, equalities due to luck are not necessarily so. The latter view, by contrast, affirms the undesirability of equalities as well as inequalities insofar (...) as they are due to luck. The symmetrical view, we argue, is by far the more compelling, both by internal luck egalitarian standards and in light of the external rightist emphasis on choice and responsibility to which luck egalitarianism may partly be seen as a response. Our main case for the symmetrical view is that when some people, against a background of equal opportunities, do not exercise their responsibility to the same degree as others, they cannot justifiably call for equalizing measures to be put in place. Indeed, such measures would be positively unfair. The symmetrical view, accordingly, rejects compensation in such cases, whereas the asymmetrical view, implausibly, enjoins it. We also examine two objections to this argument. First, that this view fails to qualify as genuinely egalitarian, instead collapsing the notion of equality into the notion of desert. Second, that the opposing asymmetrical view, in contrast to the symmetrical view, can draw support from its compatibility with sufficientarian concerns. Both objections are rebutted. We conclude that luck egalitarians are best served by endorsing the symmetrical, luck-neutralizing stance. (shrink)

There is growing concern that decision-making informed by machine learning (ML) algorithms may unfairly discriminate based on personal demographic attributes, such as race and gender. Scholars have responded by introducing numerous mathematical definitions of fairness to test the algorithm, many of which are in conflict with one another. However, these reductionist representations of fairness often bear little resemblance to real-life fairness considerations, which in practice are highly contextual. Moreover, fairness metrics tend to be implemented in narrow and targeted toolkits that (...) are difficult to integrate into an algorithm’s broader ethical assessment. In this paper, we derive lessons from ethical philosophy and welfare economics as they relate to the contextual factors relevant for fairness. In particular we highlight the debate around acceptability of particular inequalities and the inextricable links between fairness, welfare and autonomy. We propose Key Ethics Indicators (KEIs) as a way towards providing a more holistic understanding of whether or not an algorithm is aligned to the decision-maker’s ethical values. (shrink)