用最优传输方法解决多敏感变量下的公平性问题
EquiPy: Sequential Fairness using Optimal Transport in Python
- 基于最优传输理论分解多敏感变量的公平性难题
- 在真实人口普查数据上验证了算法有效性
- 适合关注算法公平性的研究人员与工程师
算法公平性因多种预测型AI系统被发现对群体子集存在不公平偏见而受到广泛关注。尽管已有诸多缓解偏差的方法，但在涉及多个敏感变量（如性别与种族的组合）时，往往难以提供准确估计和透明修正机制。本文提出开源Python工具包EquiPy，提供一个易于使用、模型无关的框架，可高效实现多敏感变量下的公平性。该工具利用理论结果将多变量带来的复杂性分解为更易求解的子问题，并配备全面的可视化工具，帮助用户在全局背景下理解各敏感变量的影响。我们在来自美国人口普查的公开数据上展示了其在偏差缓解与解释方面的易用性，并提供了使用示例代码。
原文摘要 · Abstract (English)
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.
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