EquiPy：在 Python 中使用最优传输实现序列公平性
EquiPy: Sequential Fairness using Optimal Transport in Python
AI总结：
本文推出开源 Python 包 EquiPy，基于最优传输理论将多敏感变量公平性问题分解求解，提供模型无关、可视化可解释的偏见缓解工具，并在美国人口普查数据上验证。
摘要：
算法公平性受到了相当多的关注，因为各类预测性 AI 系统已被发现存在对人口子群体不公平偏见的失效情况。人们提出了许多方法来缓解预测系统中的此类偏见；然而，当涉及多个敏感变量（例如性别与种族的组合）时，这些方法往往难以提供准确的估计和透明的纠正机制。本文介绍了一个新的开源 Python 包 EquiPy，它提供了一个易于使用且与模型无关的工具箱，用于高效实现跨多个敏感变量的公平性。它还提供全面的图形工具，使用户能够在全局背景下解释每个敏感变量的影响。EquiPy 利用相关理论结果，将使用多个变量所产生的复杂性分解为更易求解的子问题。我们在源自美国人口普查的公开可用数据上展示了该包在偏见缓解和解释方面的易用性，并提供了其使用示例代码。
英文摘要：
Algorithmic fairness has received considerable attention due to the failures of various predictive AI systems that have been found to be unfairly biased against subgroups of the population. Many approaches have been proposed to mitigate such biases in predictive systems, however, they often struggle to provide accurate estimates and transparent correction mechanisms in the case where multiple sensitive variables, such as a combination of gender and race, are involved. This paper introduces a new open source Python package, EquiPy, which provides a easy-to-use and model agnostic toolbox for efficiently achieving fairness across multiple sensitive variables. It also offers comprehensive graphic utilities to enable the user to interpret the influence of each sensitive variable within a global context. EquiPy makes use of theoretical results that allow the complexity arising from the use of multiple variables to be broken down into easier-to-solve sub-problems. We demonstrate the ease of use for both mitigation and interpretation on publicly available data derived from the US Census and provide sample code for its use.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
