FairPlay让多方协作去伪化数据集，提升AI公平性。
FairPlay: A Collaborative Approach to Mitigate Bias in Datasets for Improved AI Fairness
- 通过在线协作工具让不同立场方共同修正数据偏差
- 用户平均5轮内达成共识，显著提升谈判效率
- 无需统一公平标准，适合多元利益相关方使用
决策公平性问题至关重要，尤其在各利益相关方对公平的定义存在冲突时。采用多视角协同机制可替代单一公平标准。我们提出一款名为FairPlay的网页应用，支持多方协作去除非公平数据。在缺乏系统化协商流程和动态修改观察能力的情况下，达成共识极为困难。通过用户研究发现，参与者平均仅需约五轮交互即可达成一致，验证了该工具在提升AI系统公平性方面的潜力。
原文摘要 · Abstract (English)
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.
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