Balancing Efficiency with Equality: Auction Design with Group Fairness Concerns

TL;DR

This paper explores designing auction mechanisms that balance fairness for groups and individuals with revenue preservation, proposing new mechanisms and validating their effectiveness through experiments.

Contribution

It introduces two novel auction mechanisms that incorporate group and individual fairness while maintaining incentive compatibility.

Findings

The Group Probability Mechanism achieves group fairness and incentive compatibility.

The Group Score Mechanism extends fairness to individual level.

Experimental results show a trade-off between fairness and seller revenue.

Abstract

The issue of fairness in AI arises from discriminatory practices in applications like job recommendations and risk assessments, emphasising the need for algorithms that do not discriminate based on group characteristics. This concern is also pertinent to auctions, commonly used for resource allocation, which necessitate fairness considerations. Our study examines auctions with groups distinguished by specific attributes, seeking to (1) define a fairness notion that ensures equitable treatment for all, (2) identify mechanisms that adhere to this fairness while preserving incentive compatibility, and (3) explore the balance between fairness and seller's revenue. We introduce two fairness notions-group fairness and individual fairness-and propose two corresponding auction mechanisms: the Group Probability Mechanism, which meets group fairness and incentive criteria, and the Group Score Mechanism, which also encompasses individual fairness. Through experiments, we validate these mechanisms' effectiveness in promoting fairness and examine their implications for seller revenue.