Bridging Machine Learning and Mechanism Design towards Algorithmic Fairness

Abstract

Decision-making systems increasingly orchestrate our world: how to intervene on the algorithmic components to build fair and equitable systems is therefore a question of utmost importance; one that is substantially complicated by the context-dependent nature of fairness and discrimination. Modern systems incorporate machinelearned predictions in broader decision-making pipelines, implicating concerns like constrained allocation and strategic behavior that are typically thought of as mechanism design problems. Although both machine learning and mechanism design have individually developed frameworks for addressing issues of fairness and equity, in some complex decision-making systems, neither framework is individually sufficient. In this paper, we develop the position that building fair decision-making systems requires overcoming these limitations which, we argue, are inherent to the individual frameworks of machine learning and mechanism design. Our ultimate objective is to build an encompassing framework that cohesively bridges the individual frameworks. We begin to lay the ground work towards achieving this goal by comparing the perspective each individual discipline takes on fair decision-making, teasing out the lessons each field has taught and can teach the other, and highlighting application domains that require a strong collaboration between these disciplines.

Optimizing Generalized Gini Indices for Fairness in Rankings

“…In the broader fair machine learning community, several authors advocated for economic concepts [30], using inequality indices to quantify and mitigate unfairness [38,45,67,75], taking an axiomatic perspective [18,34,76] or applying welfare economics principles [39,61]. GGFs, in particular, were recently applied to fair multiagent reinforcement learning, with multiple reward functions [13,65,85].…”

Optimizing generalized Gini indices for fairness in rankings

“…In the broader fair machine learning community, several authors advocated for economic concepts [30], using inequality indices to quantify and mitigate unfairness [38,45,67,75], taking an axiomatic perspective [18,34,76] or applying welfare economics principles [39,61]. GGFs, in particular, were recently applied to fair multiagent reinforcement learning, with multiple reward functions [13,65,85].…”