FairPlay: A Collaborative Approach to Mitigate Bias in Datasets for Improved AI Fairness
This addresses the challenge of reconciling conflicting fairness demands among stakeholders in AI systems, offering a practical solution for collaborative bias mitigation.
The paper tackles the problem of achieving fairness in AI decision-making by introducing FairPlay, a web-based tool that enables multiple stakeholders to collaboratively debias datasets through negotiation, with user studies showing consensus reached in about five rounds.
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.