Bridging Machine Learning and Mechanism Design Towards Algorithmic Fairness

Jessie Finocchiaro, Roland Maio, Faidra Monachou, Gourab Patro, Ana-Andreea Stoica, Manish Raghavan, Stratis Tsirtsis

Abstract: Decision-making systems increasingly orchestrate our world: how to intervene on thealgorithmic components to build fair and equitable systems is therefore a question of ut-most importance; one that is substantially complicated by the context-dependent natureof fairness and discrimination. Modern decision-making systems that involve allocatingresources or information to people (e.g., school choice, advertising) incorporate machine-learned predictions in their pipelines, raising concerns about potential strategic behavioror constrained allocation, concerns usually tackled in the context of mechanism design.Although both machine learning and mechanism design have developedframeworks foraddressing issues of fairness and equity, in some complex decision-making systems, nei-ther framework is individually sufficient. In this paper, we develop the position that building fair decision-making systems requires overcoming these limitations which, weargue, are inherent to each field. Our ultimate objective is to build anencompassingframework that cohesively bridges the individual frameworks of mechanism design and machine learning. We begin to lay the ground work towards this goal by comparing the perspective each 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.