When machine learning (ML) models are used in applications that involve
humans (e.g., online recommendation, school admission, hiring, lending), the
model itself may trigger changes in the distribution of targeted data it aims
to predict. Performative prediction (PP) is a framework that explicitly
considers such model-dependent distribution shifts when learning ML models.
While significant efforts have been devoted to finding performative stable (PS)
solutions in PP for system robustness, their societal implications are less
explored and it is unclear whether PS solutions are aligned with social norms
such as fairness. In this paper, we set out to examine the fairness property of
PS solutions in performative prediction. We first show that PS solutions can
incur severe polarization effects and group-wise loss disparity. Although
existing fairness mechanisms commonly used in literature can help mitigate
unfairness, they may fail and disrupt the stability under model-dependent
distribution shifts. We thus propose novel fairness intervention mechanisms
that can simultaneously achieve both stability and fairness in PP settings.
Both theoretical analysis and experiments are provided to validate the proposed
method.

通过对执行预测的稳定解的公平性进行研究，我们发现在模型依赖的分布变化情况下，现有的公平机制可能会引发严重的极化效应和组内损失差异，并提出了一种能够同时实现稳定和公平的新的公平干预机制。