Like American politics, the academic debate over justice is polarized, with almost all theories of justice falling within one of two traditions: egalitarianism and libertarianism. This book provides an alternative to the partisan standoff by focusing not on equality or liberty, but on the idea that we should give people the things that they deserve. Mulligan argues that a just society is a meritocracy, in which equal opportunity prevails and social goods are distributed strictly on the basis of merit. That (...) gives citizens their just deserts. In addition to its novel conceptual approach, meritocracy is distinctive from existing work in two ways. First, it is grounded in research on how people actually think about justice. Empirical research reveals that people don't think that social goods should be distributed equally. Nor do they dismiss the idea of social justice. Across ideological and cultural lines, people want rewards to reflect merit. Second, the book discusses hot-button political issues and makes concrete policy recommendations. These issues include anti-meritocratic bias against women and racial minorities and the United States’ widening economic inequality. Justice and the Meritocratic State offers a new theory of justice and provides solutions to our most vexing social and economic problems. It will be of keen interest to philosophers, economists, and political theorists. (shrink)

This paper examines common and past applications of affirmative action, including arguments for and against the application; presents John Rawls’ theory of justice, and proposes a framework using Rawls’ theory for future applications of affirmative action. The proposal relieves heavily on the principle of fair equality of opportunity, both as an indicator for when using affirmative action is appropriate as well as a tool to help identify the people that affirmative action programs should target. Using this framework, the public education (...) system is identified as a sector of society that fails to provide fair equality of opportunity for all citizens, and an example of how the Rawlsian conception of affirmative action should be used in practice to help address the inequalities and help restore equality of opportunity within the public education system is given. Objections to affirmative action, both in general and more specifically tailored to the model proposed in this paper, are raised and responded to. This paper comes to the conclusion that affirmative action programs, when targeted towards individuals who are denied fair equality of opportunity and have low rates of social mobility, will help create a more just society. (shrink)

Affirmative action continues to divide. My aim in this paper is to present participants in the debate with a new distinction, namely one between negative and positive affirmative action. Whereas positive affirmative action has to do with certain goods, such as a place at a prestigious university or a job at a prestigious company, negative affirmative action has to do with certain bads, such as a firing or a sentence. I then argue that some of the most prominent arguments in (...) favor of affirmative action speak at least as much in favor of negative as positive affirmative action. At the same time, at least one of the most prominent arguments put forward against affirmative action speak less against negative affirmative action. Thus, the paper should redraw the battle lines in the affirmative action debate. (shrink)

According to the relational egalitarian theory of justice, justice requires that people relate as equals. To relate as equals, many relational egalitarians argue, people must (i) regard each other as equals, and (ii) treat each other as equals. In this paper, we argue that, under conditions of background injustice, such relational egalitarians should endorse affirmative action in the ways in which (dis)esteem is attributed to people as part of the regard-requirement for relating as equals.

This article investigates the hitherto under-examined relations between affirmative action, paternalism and respect. We provide three main arguments. First, we argue that affirmative action initiatives are typically paternalistic and thus disrespectful towards those intended beneficiaries who oppose the initiatives in question. Second, we argue that not introducing affirmative action can also be disrespectful towards these potential beneficiaries because such inaction involves a failure to adequately recognize their moral worth. Third, we argue that the paternalistic disrespect involved in affirmative action is (...) alleviated when the potential beneficiaries’ preferences against such initiatives are adaptive. We conclude that although there is a relevant sense in which paternalistic affirmative action is disrespectful, it may well be more disrespectful not to pursue such policies. (shrink)

How should philosophers in India approach the work of John Rawls? I argue against the view that his work should be regarded as exclusively within the domain of 'Western philosophy', which needs some distinctive process of translation and contextualization in order to speak to 'Indian conditions'. I also question the idea that 'Indian political philosophy' should be seen as an autonomous discipline with roots specifically in the Indian past.

In this essay, we describe practices developed by the philosophy department at the University of Oklahoma to promote fair and inclusive recruitment, application review, and hiring for faculty positions.

Typically, fair machine learning research focuses on a single decision maker and assumes that the underlying population is stationary. However, many of the critical domains motivating this work are characterized by competitive marketplaces with many decision makers. Realistically, we might expect only a subset of them to adopt any non-compulsory fairness-conscious policy, a situation that political philosophers call partial compliance. This possibility raises important questions: how does partial compliance and the consequent strategic behavior of decision subjects affect the allocation outcomes? (...) If k% of employers were to voluntarily adopt a fairness-promoting intervention, should we expect k% progress (in aggregate) towards the benefits of universal adoption, or will the dynamics of partial compliance wash out the hoped-for benefits? How might adopting a global (versus local) perspective impact the conclusions of an auditor? In this paper, we propose a simple model of an employment market, leveraging simulation as a tool to explore the impact of both interaction effects and incentive effects on outcomes and auditing metrics. Our key findings are that at equilibrium: (1) partial compliance by k% of employers can result in far less than proportional (k%) progress towards the full compliance outcomes; (2) the gap is more severe when fair employers match global (vs local) statistics; (3) choices of local vs global statistics can paint dramatically different pictures of the performance vis-a-vis fairness desiderata of compliant versus non-compliant employers; (4) partial compliance based on local parity measures can induce extreme segregation. Finally, we discuss implications for auditors and insights concerning the design of regulatory frameworks. (shrink)

Algorithmic systems are increasingly used by state agencies to inform decisions about humans. They produce scores on risks of recidivism in criminal justice, indicate the probability for a job seeker to find a job in the labor market, or calculate whether an applicant should get access to a certain university program. In this contribution, we take an interdisciplinary perspective, provide a bird’s eye view of the different key decisions that are to be taken when state actors decide to use an (...) algorithmic system, and illustrate these decisions with empirical examples from case studies. Building on these insights, we discuss the main pitfalls and promises of the use of algorithmic system by the state and focus on four levels: The most basic question whether an algorithmic system should be used at all, the regulation and governance of the system, issues of algorithm design, and, finally, questions related to the implementation of the system on the ground and the human–machine-interaction that comes with it. Based on our assessment of the advantages and challenges that arise at each of these levels, we propose a set of crucial questions to be asked when such intricate matters are addressed. (shrink)