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

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

Keywords: Algorithmic Fairness, Causality, Debiasing, Human-in-the-loop, Visual Analytics

TL;DR: We present a web-based software application, FairPlay, that enables multiple stakeholders to debias datasets collaboratively.

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 perspective 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. We have conducted user studies that demonstrate the success of FairPlay, with users reaching consensus within about five rounds of gameplay, illustrating the application’s potential for enhancing fairness in AI systems.

Submission Number: 43