Post-processing mitigation techniques for group fairness generally adjust the
decision threshold of a base model in order to improve fairness. Methods in
this family exhibit several advantages that make them appealing in practice:
post-processing requires no access to the model training pipeline, is agnostic
to the base model architecture, and offers a reduced computation cost compared
to in-processing. Despite these benefits, existing methods face other
challenges that limit their applicability: they require knowledge of the
sensitive attributes at inference time and are oftentimes outperformed by
in-processing. In this paper, we propose a general framework to transform any
in-processing method with a penalized objective into a post-processing
procedure. The resulting method is specifically designed to overcome the
aforementioned shortcomings of prior post-processing approaches. Furthermore,
we show theoretically and through extensive experiments on real-world data that
the resulting post-processing method matches or even surpasses the
fairness-error trade-off offered by the in-processing counterpart.

提出了针对群体公平性的后处理缓解技术方法，通过调整基础模型的决策阈值，以提高公平性。在现实应用中，这种方法具有许多优势：后处理不需要访问模型训练流程，对基础模型架构没有偏好，并且计算成本较低。然而，现有方法仍面临其他挑战，如需要推理时了解敏感属性，并且常常被内部处理方法优于。本文提出了一个通用框架，将带有惩罚目标的内部处理方法转化为后处理过程，以克服先前后处理方法的缺点。此外，我们通过理论和对实际数据的大量实验表明，该后处理方法在公平性和错误折衷方面与内部处理相当甚至更好。

FRAPPÉ: 一个适用于群体公平约束的后处理框架

FRAPPÉ: A Post-Processing Framework for Group Fairness Regularization

As Machine Learning technologies become increasingly used in contexts that
affect citizens, companies as well as researchers need to be confident that
their application of these methods will not have unexpected social
implications, such as bias towards gender, ethnicity, and/or people with
disabilities. There is significant literature on approaches to mitigate bias
and promote fairness, yet the area is complex and hard to penetrate for
newcomers to the domain. This article seeks to provide an overview of the
different schools of thought and approaches to mitigating (social) biases and
increase fairness in the Machine Learning literature. It organises approaches
into the widely accepted framework of pre-processing, in-processing, and
post-processing methods, subcategorizing into a further 11 method areas.
Although much of the literature emphasizes binary classification, a discussion
of fairness in regression, recommender systems, unsupervised learning, and
natural language processing is also provided along with a selection of
currently available open source libraries. The article concludes by summarising
open challenges articulated as four dilemmas for fairness research.

这篇文章介绍了机器学习中的社会偏见和公平性问题，总结了预处理、处理和后处理等多种方法，包含二分类、回归、推荐系统、无监督学习和自然语言处理等多个方面，同时提供了多个开源库。最后，列举了公平性研究的四个难题。