We analyze a frequent but undertheorized form of structural injustice, one that arises due to the difficulty of reaching numerically equitable representation of underrepresented subgroups within a larger group. This form of structural injustice is significant because it could occur even if it were possible to completely eliminate bias and overt discrimination from hiring and recruitment practices. The conceptual toolkit we develop can be used to analyze such situations and propose remedies. Specifically, based on a simple mathematical model, we offer (...) a new argument in favour of quotas, explore implications for policy-making, and consider the wider philosophical significance of the problem. We show that in order to reach more equitable representations, quota-based recruitment may often be practically unavoidable. Assuming that members of groups in statistical minority are more likely to quit due to their marginalization, their proportions can stabilize at a low level, preventing a shift towards more equal representation and conserving the minority status of the subgroup. We show that this argument has important implications for addressing, preventing, and remediating the structural injustice of unfair representation. (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)

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)

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)

This paper proposes an application of Enrique Dussel’s ethics of liberation to an issue of crucial importance to US minorities: the debate on affirmative action. Over the past fifty years, this debate has been framed in terms of the opposition between advocates of affirmative action who claim that it is needed in order to achieve the integration and participation of traditionally oppressed groups to society without which there is no equality of rights, and critics who argue that affirmative action violates (...) equality by enforcing a double standard that undermines the ideal of a color-blind society. In this paper, I show how the basic principles of Dussel’s ethical theory (which are best expounded in his book Ethics of Liberation) allow us to address what I take to be the main demands of both advocates and critics of affirmative action in a satisfactory way. (shrink)

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)

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.