Bridging Machine Learning and Mechanism Design towards Algorithmic Fairness

TL;DR

This paper advocates for integrating machine learning and mechanism design to develop fairer decision-making systems, addressing their individual limitations and emphasizing interdisciplinary collaboration for complex applications.

Contribution

It proposes a unified framework combining machine learning and mechanism design perspectives to improve fairness in decision-making systems.

Findings

Comparison of fairness perspectives in ML and mechanism design

Identification of complementary strengths of both fields

Highlighting application domains needing interdisciplinary approaches

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 decision-making systems that involve allocating resources or information to people (e.g., school choice, advertising) incorporate machine-learned predictions in their pipelines, raising concerns about potential strategic behavior or constrained allocation, concerns usually tackled in the context of mechanism design. Although both machine learning and mechanism design have 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 each field. Our ultimate objective is to build an encompassing framework 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.