Fairness-Aware Meta-Learning via Nash Bargaining
CoRR(2024)
摘要
To address issues of group-level fairness in machine learning, it is natural
to adjust model parameters based on specific fairness objectives over a
sensitive-attributed validation set. Such an adjustment procedure can be cast
within a meta-learning framework. However, naive integration of fairness goals
via meta-learning can cause hypergradient conflicts for subgroups, resulting in
unstable convergence and compromising model performance and fairness. To
navigate this issue, we frame the resolution of hypergradient conflicts as a
multi-player cooperative bargaining game. We introduce a two-stage
meta-learning framework in which the first stage involves the use of a Nash
Bargaining Solution (NBS) to resolve hypergradient conflicts and steer the
model toward the Pareto front, and the second stage optimizes with respect to
specific fairness goals. Our method is supported by theoretical results,
notably a proof of the NBS for gradient aggregation free from linear
independence assumptions, a proof of Pareto improvement, and a proof of
monotonic improvement in validation loss. We also show empirical effects across
various fairness objectives in six key fairness datasets and two image
classification tasks.
更多查看译文
AI 理解论文
溯源树
样例
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要