反事实公平的图像分类器是否满足群体公平性？——理论与实证研究
Do Counterfactually Fair Image Classifiers Satisfy Group Fairness? -- A Theoretical and Empirical Study
AI总结：
研究图像分类中反事实公平与群体公平的关系，构建新数据集评估二者，发现CF不意味着GF，原因是潜在属性G，提出CKD基线，实验表明减少对G的依赖可使实现CF的模型满足GF。
摘要：
算法公平性的概念已从公平的各个方面积极探索，如反事实公平（CF）和群体公平（GF）。然而，CF和GF之间的确切关系仍不清楚，特别是在图像分类任务中，因为难以从现有图像中收集评估CF所需的反事实样本。本文利用高质量图像编辑方法构建新图像数据集并人工标注，以同时评估CF和GF。实证发现图像分类中CF不意味着GF，而表格数据集则相反。理论表明这可能是由于存在与敏感属性相关但非其导致的潜在属性G。为此提出反事实知识蒸馏（CKD）基线来减轻与敏感属性的相关性，实验表明若成功减少对G的依赖，实现CF的模型可满足GF。
英文摘要：
The notion of algorithmic fairness has been actively explored from various aspects of fairness, such as counterfactual fairness (CF) and group fairness (GF). However, the exact relationship between CF and GF remains to be unclear, especially in image classification tasks; the reason is because we often cannot collect counterfactual samples regarding a sensitive attribute, essential for evaluating CF, from the existing images (\eg, a photo of the same person but with different secondary sex characteristics). In this paper, we construct new image datasets for evaluating CF by using a high-quality image editing method and carefully labeling with human annotators. Our datasets, \oursceleb and \ourslfw, build upon the popular image GF benchmarks; hence, we can evaluate CF and GF simultaneously. We empirically observe that CF does not imply GF in image classification, whereas previous studies on tabular datasets observed the opposite. We theoretically show that it could be due to the existence of a latent attribute $G$ that is correlated with, but not caused by, the sensitive attribute (\eg, secondary sex characteristics are highly correlated with hair length). From this observation, we propose a simple baseline, Counterfactual Knowledge Distillation (CKD), to mitigate such correlation with the sensitive attributes. Extensive experimental results on \oursceleb and \ourslfw demonstrate that CF-achieving models satisfy GF if we successfully reduce the reliance on $G$ (\eg, using CKD).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
