人口偏差数据集的指标:面部表情识别的案例研究
IEEE transactions on pattern analysis and machine intelligence
|February 5, 2024
概括
本研究通过审查和分类用于测量代表不平衡的指标来解决机器学习数据集中的人口偏差. 面部情感识别 (FER) 的一个案例研究显示,一组较小的指标可以有效量化偏见.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习伦理学 机器学习伦理学
背景情况:
- 数据集中的人口偏差,特别是群体代表性的统计失衡,是机器学习 (ML) 模型中不公平和歧视的关键原因.
- 现有的量化这些偏见的指标是多样化的,经常从其他学科借用,需要一个结构化的选择和应用方法.
研究的目的:
- 审查和分类现有的指标来衡量数据集中的人口偏差.
- 为选择适当的偏差测量指标制定一个实际的分类学.
- 通过案例研究分析偏见指标的实际特征和冗余性.
主要方法:
- 对量化人口代表不平衡指标的综合文献综述.
- 为这些指标制定分类分类法.
- 实证案例研究涉及20个面部情绪识别 (FER) 数据集来分析偏差测量指标.
主要成果:
- 识别和分类了一系列用于评估数据集人口偏差的指标.
- 案例研究表明,许多现有的偏见测量指标是多余的.
- 较少的指标子集可能足以有效地测量数据集中的人口偏差.
结论:
- 拟议的框架和分类学有助于研究人员选择适当的偏差测量指标.
- 研究结果表明,一组精简的指标可以有效量化数据集偏差.
- 提供可操作的见解,以减轻数据集偏差,提高AI模型的公平性和准确性.
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