Tutorial @ ACM EC 2021

MD4SG members Jessie Finocchiaro, Edwin Lock, Faidra Monachou, and Manish Raghavan organized a tutorial on Fairness and Discrimination in Mechanism Design and Machine Learning at the ACM Conference on Economics and Computation (EC) 2021.

This tutorial aimed to bridge notions of algorithmic fairness between the machine learning and mechanism design communities, particularly in resource allocation. While mechanism design is often used in inherently social and human domains, such as matching and admitting students to schools, machine learning algorithms tackle similar problems, but often from a different perspective involving large-scale automation of decisions using Big Data.

Motivated by applications in labor, education and other settings, the tutorial gave an overview of classic and recent results in the economics of discrimination, then discussed applied research questions where mechanism design and machine learning can be applied to mitigate discrimination or detect bias. The tutorial discusses the connections between two emerging research directions: sharing economy and (online) labor markets and equity and fairness in education.