Fairness-Aware Information Design: Bridging Algorithmic Fairness and Strategic Signaling

Finocchiaro et al. (2020) argue that neither machine learning nor mechanism design alone can ensure fairness in complex systems. This research direction proposes new mechanisms that jointly optimize for strategic behavior (in signaling or information revelation) and formal notions of fairness (e.g., demographic parity, equal opportunity) from ML. For instance, in school choice or ad auctions, can we design signaling schemes that not only induce truthful or efficient equilibria but also guarantee fair treatment across groups? This synthesis would require new theory—perhaps adapting fairness constraints to the equilibrium analysis of mechanism design—and could leverage side information (Balcan et al. 2023) or behavioral models for richer, more realistic settings. The societal significance is high: many real-world mechanisms (ad serving, loan approval, etc.) are both strategic and subject to fairness scrutiny, and existing tools often fall short when these concerns intersect.

References:

1. Bicriteria Multidimensional Mechanism Design with Side Information. Maria-Florina Balcan, Siddharth Prasad, T. Sandholm (2023). Neural Information Processing Systems.
2. Bridging Machine Learning and Mechanism Design towards Algorithmic Fairness. J. Finocchiaro, R. Maio, F. Monachou, Gourab K. Patro, Manish Raghavan, Ana-Andreea Stoica, Stratis Tsirtsis (2020). Conference on Fairness, Accountability and Transparency.