一种精细的重称技术,用于非歧视性分类
Yuefeng Liang1, Cho-Jui Hsieh2, Thomas C M Lee1
1Department of Statistics, University of California at Davis, CA, United States of America.
PloS one
|August 20, 2024
概括
这项研究引入了一种精细的重权衡技术,用于机器学习,以减少歧视. 通过考虑敏感和不敏感的属性,它可以提高公平性,以最小的准确性损失和增强的可扩展性.
科学领域:
- 机器学习 机器学习
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 机器学习系统可以延续和加剧社会经济差异.
- 现有的歧视意识分类方法往往只关注敏感属性.
研究的目的:
- 提出一种新的数据预处理技术,以减少机器学习中的歧视.
- 通过结合敏感和不敏感的属性来完善实例权重.
主要方法:
- 开发了一种数据预处理技术,将权重分配给训练实例.
- 公式重量分配作为一个线性编程问题.
- 包含了敏感和不敏感的属性,以提高重量.
主要成果:
- 在对分类准确性的最小影响下,实现了显著的歧视减少.
- 与现有的预处理方法相比,证明了更高的可扩展性.
- 启用了明确监控公平性和准确性之间的权衡.
结论:
- 精细的重权方法有效地减少了歧视,而不会改变输入数据或标签.
- 这种方法提供了一个可扩展和透明的解决方案,以提高机器学习模型的公平性.
- 该方法为用户提供了对公平性-准确性平衡的控制权.
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