研究回归模型在肾移植中的公平性，发现年龄歧视更严重
Fairness Perceptions in Regression-based Predictive Models
- 提出三种基于差异的群体公平性度量方法
- 85人调研显示对分离与充分性准则偏好明显
- 模型对性别种族公平，但对年龄群体不公平
基于回归的预测分析在现代肾移植中常继承训练数据中的偏见，导致社会歧视和器官利用效率低下，尤其影响少数社会群体。尽管存在此问题，针对回归模型公平性的研究仍有限。本文引入三种新的基于差异的群体公平性概念：(i) 独立性，(ii) 分离性，(iii) 充分性，用于评估回归分析工具的公平性。同时通过众包平台 Prolific 招募 85 名参与者，采用混合逻辑离散选择模型分析公众对公平性的反馈，以识别社会可接受的公平标准。研究结果明确显示，公众强烈偏好分离性和充分性公平概念；预测模型在性别和种族维度上被视为公平，但在年龄维度上被认为不公平。
原文摘要 · Abstract (English)
Regression-based predictive analytics used in modern kidney transplantation is known to inherit biases from training data. This leads to social discrimination and inefficient organ utilization, particularly in the context of a few social groups. Despite this concern, there is limited research on fairness in regression and its impact on organ utilization and placement. This paper introduces three novel divergence-based group fairness notions: (i) independence, (ii) separation, and (iii) sufficiency to assess the fairness of regression-based analytics tools. In addition, fairness preferences are investigated from crowd feedback, in order to identify a socially accepted group fairness criterion for evaluating these tools. A total of 85 participants were recruited from the Prolific crowdsourcing platform, and a Mixed-Logit discrete choice model was used to model fairness feedback and estimate social fairness preferences. The findings clearly depict a strong preference towards the separation and sufficiency fairness notions, and that the predictive analytics is deemed fair with respect to gender and race groups, but unfair in terms of age groups.
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