人工智能偏见作为不对称性:一个指导实践的审查
Gabriella Waters1, Phillip Honenberger1
1Center for Equitable AI and Machine Learning Systems (CEAMLS), Morgan State University, Baltimore, MD, United States.
Frontiers in big data
|October 29, 2025
概括
人工智能 (AI) 的偏见正在从一个错误转变为一个不可分割的组成部分,有时甚至更可取. 本文将AI偏见重新定义为对称性违反,并指导如何接受或最小化特定偏见以改善AI开发.
科学领域:
- 人工智能的人工智能
- 计算机科学 计算机科学
- 人工智能中的伦理学
背景情况:
- 人工智能系统中偏见的传统观点仅仅是错误或缺陷正在受到挑战.
- 新兴的观点认识到偏见是人工智能的固有,有时是有益的方面.
研究的目的:
- 重新评估人工智能系统中偏差的理解和测量.
- 提供关于哪些AI偏差可以接受或放大,哪些可以最大限度地减少的指导.
- 解决AI偏见及其影响的不断变化的局面.
主要方法:
- 审查理解人工智能偏见的范式转变.
- 将偏见定义为"违反对称标准" (跟随凯利).
- 将有问题的偏见分类为错误的表现,不公平的待遇和违反过程理想.
主要成果:
- 在对称性标准定义下,许多人工智能偏见是良性的.
- 确定了三大关键类别的不良AI偏见:错误的表示,不公平的待遇和违反过程理想.
- 在AI开发和应用管道中突出了潜在的偏差发生点.
结论:
- 提出了理解和测量人工智能偏差的新框架,重点关注对称性违规.
- 为区分人工智能中可接受和不受欢迎的偏见提供了指导.
- 这项研究提供了一种细微的方法来管理人工智能开发和部署中的偏见.
关键词:
人工智能的人工智能是人工智能.偏见 偏见 偏见 偏见 偏见偏差差异的权衡偏差差异的权衡.认知偏见是一种认知偏见.诱导偏差是一种诱导性偏差.机器学习是机器学习.统计偏差是一种统计偏差.对称性对称性对称性对称性对称性对称性更多相关视频
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