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

Clustering algorithms are widely utilized for many modern data science
applications. This motivates the need to make outputs of clustering algorithms
fair. Traditionally, new fair algorithmic variants to clustering algorithms are
developed for specific notions of fairness. However, depending on the
application context, different definitions of fairness might need to be
employed. As a result, new algorithms and analysis need to be proposed for each
combination of clustering algorithm and fairness definition. Additionally, each
new algorithm would need to be reimplemented for deployment in a real-world
system. Hence, we propose an alternate approach to group-level fairness in
center-based clustering inspired by research on data poisoning attacks. We seek
to augment the original dataset with a small number of data points, called
antidote data. When clustering is undertaken on this new dataset, the output is
fair, for the chosen clustering algorithm and fairness definition. We formulate
this as a general bi-level optimization problem which can accommodate any
center-based clustering algorithms and fairness notions. We then categorize
approaches for solving this bi-level optimization for two different problem
settings. Extensive experiments on different clustering algorithms and fairness
notions show that our algorithms can achieve desired levels of fairness on many
real-world datasets with a very small percentage of antidote data added. We
also find that our algorithms achieve lower fairness costs and competitive
clustering performance compared to other state-of-the-art fair clustering
algorithms.

为了解决聚类算法输出不公平的问题，我们提出了一种使用少量辅助数据来实现中心聚类的群体公平性的方法，将其建模为一个通用的双层优化问题，并分类并解决了两种不同问题设置的优化方法。我们的实验表明，与其他最先进的公平聚类算法相比，我们的算法可以在添加极少的辅助数据的情况下实现所需的公平水平，并且具有更低的公平成本和可比较的聚类性能。