From Fair Decision Making To Social Equality

By Hussein Mozannar, Mesrob I. Ohannessian, Nati Srebro. 2018

105 citations recorded by the source corpus.

The study of fairness in intelligent decision systems has mostly ignored long-term influence on the underlying population. Yet fairness considerations (e.g. affirmative action) have often the implicit goal of achieving balance among groups within the population. The most basic notion of balance is eventual equality between the qualifications of the groups. How can we incorporate influence dynamics in decision making? How well do dynamics-oblivious fairness policies fare in terms of reaching equality? In this paper, we propose a simple yet revealing model that encompasses (1) a selection process where an institution chooses from multiple groups according to their qualifications so as to maximize an institutional utility and (2) dynamics that govern the evolution of the groups' qualifications according to the imposed policies. We focus on demographic parity as the formalism of affirmative action. We first give conditions under which an unconstrained policy reaches equality on its own. In this case, surprisingly, imposing demographic parity may break equality. When it doesn't, one would expect the additional constraint to reduce utility, however, we show that utility may in fact increase. In real world scenarios, unconstrained policies do not lead to equality. In such cases, we show that although imposing demographic parity may remedy it, there is a danger that groups settle at a worse set of qualifications. As a silver lining, we also identify when the constraint not only leads to equality, but also improves all groups. These cases and trade-offs are instrumental in determining when and how imposing demographic parity can be beneficial in selection processes, both for the institution and for society on the long run.

Subfields: Economics and Econometrics, Sociology and Political Science

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

In Sociology and Political Science

Citations in the corpus

Beyond Individual and Group Fairness (2020, background): “Recent works have also studied the long-term impact of optimizing fairness criteria in settings with feedback mechanisms [Liu et al., 2018, Hashimoto et al., 2018, Mouzannar et al., 2019, Kannan et al., 2019].”
Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approach (2020, background): “Fairness in dynamical systems has been explored in speciﬁc domains: predictive policing (Lum & Isaac, 2016; Ensign et al., 2018), hiring (Hu & Chen, 2018; Hu et al., 2019), lending (Mouzannar et al., 2019), and RSs (Chaney et al., 2018; Bountouridis et al., 2019).”
Pretrained AI Models: Performativity, Mobility, and Change (2019, background): “lling prophecy when new training data is used to update models that capture this under investment by disadvantaged subpopulations. In the AI context, recent mathematical models of AI model retraining [33, 67] capture this dynamic phenomenon of populations becoming more like what (biased) models pred”
Optimal Decision Making Under Strategic Behavior (2019, result): “mentally different and their technical contributions are orthogonal to ours. More broadly, our work also relates to several recent studies on the long-term consequences of machine learning algorithms [10, 15, 17, 21] and recommender systems [19, 20]. Once we focus on the utility of a decision policy”
Fair Decisions Despite Imperfect Predictions (2019, background): “The work most closely related to ours analyzes the long-term effects of consequential decisions informed by data-driven predictive models on underrepresented groups (Hu & Chen, 2018; Liu et al., 2018; Mouzannar et al., 2019; Tabibian et al., 2019).”