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.

为了解决机器学习中群体层面公平性的问题，我们提出了一个两阶段元学习框架，第一阶段使用纳什谈判解决超梯度冲突，将模型引导到帕累托前沿，第二阶段优化特定的公平目标。

公平感知元学习与纳什谈判

Fairness-Aware Meta-Learning via Nash Bargaining

Modern classification problems exhibit heterogeneities across individual
classes: Each class may have unique attributes, such as sample size, label
quality, or predictability (easy vs difficult), and variable importance at
test-time. Without care, these heterogeneities impede the learning process,
most notably, when optimizing fairness objectives. Confirming this, under a
gaussian mixture setting, we show that the optimal SVM classifier for balanced
accuracy needs to be adaptive to the class attributes. This motivates us to
propose CAP: An effective and general method that generates a class-specific
learning strategy (e.g. hyperparameter) based on the attributes of that class.
This way, optimization process better adapts to heterogeneities. CAP leads to
substantial improvements over the naive approach of assigning separate
hyperparameters to each class. We instantiate CAP for loss function design and
post-hoc logit adjustment, with emphasis on label-imbalanced problems. We show
that CAP is competitive with prior art and its flexibility unlocks clear
benefits for fairness objectives beyond balanced accuracy. Finally, we evaluate
CAP on problems with label noise as well as weighted test objectives to
showcase how CAP can jointly adapt to different heterogeneities.

现代分类问题存在类内异质性：每个类别可能具有唯一的属性，如样本大小、标签质量或可预测性（易 vs 难），以及测试时的变量重要性。我们提出了 CAP 方法，该方法根据类别属性生成特定于类别的学习策略（例如超参数），优化过程更好地适应这些异质性。CAP 在损失函数设计和事后 logit 调整方面具有竞争力，并在公平目标超过平衡准确性时展现明显优势。最后，我们评估了 CAP 方法在存在标签噪声和加权测试目标的问题上，展示了 CAP 方法如何同时适应不同的异质性。