Bridging Machine Learning and Mechanism Design Towards Algorithmic Fairness
Published in ACM Conference on Fairness, Accountability and Transparency (FAccT) 2021. Originally appeared at AI for Social Good (AI4SG) Workshop at Harvard CRCS., 2020
Abstract: As fairness and discrimination concerns permeate the design of both machine learning algorithms and mechanism design problems, we discuss differences in approaches between these two fields. We aim to bridge these two communities into a cohesive narrative that encompasses both the large-scale capabilities of machine learning and group-focused fairness as well as the strategic incentives and utility-based notions of fairness from mechanism de-sign, showing their necessity in designing a fair pipeline.
Workshop paper here Workshop slides Poster
This collaboration inspired a workshop in 2021 at the ACM Conference on Economics and Computation. Tutorial Website