公平与风险:一个集团的伦理论点 公平的定义 保险公司可以使用
Joachim Baumann1,2, Michele Loi3
1Department of Informatics, University of Zurich, Zurich, Switzerland.
概括
保险中的算法预测可能会导致由于偏见的风险模型而导致不公平的保费. 本研究提出了充分性标准 (良好校准) 作为评估保险定价中的集团公平性的公平替代方案.
科学领域:
- 数据科学数据科学数据科学
- 保险分析 保险分析
- 机器学习伦理学 机器学习伦理学
背景情况:
- 算法预测为保险费用提供了个性化的风险模型.
- 这些算法的潜在偏见可能导致对特定群体的歧视和社会不公正.
- 确保算法保险定价的公平性至关重要,以防止系统性不利.
研究的目的:
- 分析保险保费系统中的算法公平性.
- 在保险环境中定义和衡量公平性.
- 确定适当的公平标准来评估保费中的算法偏差.
主要方法:
- 已建立的公平的机器学习公平性框架应用于保险费.
- 评估集团公平性标准,如独立性 (统计/人口统计平价) 和分离 (均赔率).
- 建议并证明充分性标准 (井口校准) 是一个合适的替代方案.
主要成果:
- 独立性和分离标准被认为不适合保险费.
- 充分性标准 (良好校准) 被提出为评估集团公平性的规范性可辩护方法.
- 澄清了集团公平性与保险费个性化程度之间的关系.
结论:
- 保险公司可以使用充分性标准来评估和减轻其风险模型中的偏差.
- 采用公平的算法实践有助于避免歧视性溢价系统对声誉造成损害.
- 这项研究为开发公平可信的算法保险提供了一个框架.
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