偏见去哪里,我也会去哪里:对算法偏见缓解的综合性,系统的审查
Louis Hickman1, Christopher Huynh1, Jessica Gass1
1Department of Psychology, Virginia Tech.
The Journal of applied psychology
|April 1, 2025
概括
机器学习 (ML) 评估可以延续不平等. 本研究提出了一个四阶段模型,以减轻ML人员选择中的算法偏见,整合计算机科学和组织研究,以获得更公平的结果.
科学领域:
- 组织心理学 组织心理学
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
背景情况:
- 机器学习 (ML) 模型越来越多地用于人员选择,这引发了社会对潜在偏见和不平等加剧的担忧.
- 现有的组织心理学和法律研究研究涉及ML评估,但缺乏从计算机和数据科学中整合公平操作和偏见缓解.
研究的目的:
- 整合来自不同研究领域的公平操作和算法偏差缓解方法.
- 提出一个全面的四阶段模型,用于开发和部署ML评估与偏差缓解策略.
主要方法:
- 对算法偏差定义,法律要求 (美国和欧盟) 和偏差缓解技术的系统审查.
- 开发一个四个阶段的模型:数据生成,模型培训,模型测试和模型部署.
- 在ML评估生命周期的每个阶段确定偏见来源和公平性考虑.
主要成果:
- 拟议的四阶段模型阐明了在ML评估开发和部署过程中潜在的偏见和不公平来源.
- 整合法律要求和偏见缓解策略,确定有效和合规的方法.
- 突出了有关算法偏差缓解的当前知识的差距,强调了跨学科合作的必要性.
结论:
- 为开发和部署更公平的基于 ML 的人员评估提供了一个新的,整合性的框架.
- 为从业人员提供了关于负责任地开发和部署ML评估的建议.
- 概述了未来的研究方向,以解决算法偏见和公平性中发现的知识差距,促进组织研究人员,计算机科学家和数据科学家之间的合作.
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