Work

FairPlay: A Collaborative Approach to Mitigate Bias in Datasets for Improved AI Fairness

Year: 2025

Type: article

Abstract: The issue of fairness in decision-making is a critical one, especially given the variety of stakeholder demands for differing and mutually incompatible versions of fairness. Adopting a strategic interaction of perspectives provides an alternative to enforcing a singular standard of fairness. We present a web-based software application, FairPlay, that enables multiple stakeholders to debias datasets collaboratively. With FairPlay, users can negotiate and arrive at a mutually acceptable outcome without a universally agreed-upon theory of fairness. In the absence of such a tool, reaching a consensus would be highly challenging due to the lack of a systematic negotiation process and the inability to modify and observe changes. We have conducted user studies that demonstrate the success of FairPlay, as users could reach a consensus within about five rounds of gameplay, illustrating the application's potential for enhancing fairness in AI systems.

Source: Proceedings of the ACM on Human-Computer Interaction

DOI: 10.1145/3710982

Authors: Tina Behzad, Mithilesh Kumar Singh, Anthony J. Ripa, Klaus Mueller

Institution: Stony Brook University

Language: English

Topic: Ethics and Social Impacts of AI

Subfield: Safety Research

Field: Social Sciences

Domain: Social Sciences

SDGs: Reduced inequalities, Peace, justice, and strong institutions

Open Access status: bronze

Funder: State University of New York