PreCoF:为公平性提供反事实解释
Sofie Goethals1, David Martens1, Toon Calders2
1Department of Engineering Management, University of Antwerp, 2000 Antwerp, Belgium.
概括
本研究引入了预测反事实公平性 (PreCoF),以检测机器学习模型中的隐性偏差. PreCoF使用反事实解释来识别不公平的歧视,即使敏感属性没有直接使用.
科学领域:
- 人工智能的人工智能
- 机器学习伦理学 机器学习伦理学
- 算法公平性 算法公平性
背景情况:
- 机器学习模型在高风险决策中的风险是放大数据集偏差.
- 缺乏用于检测模型偏差的通用指标,阻碍了公平性评估.
- 了解偏见的性质对于选择适当的缓解策略至关重要.
研究的目的:
- 将可解释的人工智能 (XAI) 与公平性研究集成,以提供对模型偏见的见解.
- 开发一个新的指标,预测反事实公平性 (PreCoF),以检测明确和隐含的偏见.
- 评估反事实解释在识别不公平歧视方面的有效性.
主要方法:
- 使用 (预测性) 反事实解释来分析模型行为.
- 开发和应用PreCoF指标用于偏差检测.
- 对保护和不保护组的解释中的属性存在进行比较.
主要成果:
- 通过分析反事实解释中的属性重要性,PreCoF指标成功地检测出隐性偏差.
- 确定了模型通过相关属性而不是直接使用敏感属性的方式使受保护群体处于不利地位的例子.
- 证明了反事实解释在揭示微妙形式的算法歧视中的实用性.
结论:
- PreCoF提供了一种强大的方法来评估算法公平性,特别是隐含偏差.
- 反事实解释是审计机器学习模型公平性的有价值的工具.
- 调查结果可以为决策者提供关于自动化决策中歧视结果的理由的信息.
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