Causal Discovery for Fairness
面向公平性分析的因果发现AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要直接分析因果关系识别与因果图选择如何影响公平性及歧视判断。
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面向公平性分析的因果发现
- 研究问题
- 当公平性分析所需的因果图未知时，不同因果发现算法及其得到的图结构差异，会如何影响歧视与公平性结论？
- 方法与思路
- 作者回顾从观测数据发现因果关系的主要算法，并使用合成数据和常见公平性基准，比较不同因果模型下的结论。
- 材料陈述的贡献
- 摘要称，即使因果图只有细微差异，公平性判断也可能明显变化，因此强调因果发现步骤对因果公平性分析的影响。
- 阅读关注点
- 应核对公平性的因果定义、各算法的识别条件、图的不确定性如何处理，以及实证差异是否来自可区分的因果结构。
- 阅读边界
- 这是 arXiv 预印本，不能据此认定已同行评审或期刊接收；摘要未说明算法条件或差异幅度，不能据此确认发现图的正确性。
原文摘要
It is crucial to consider the social and ethical consequences of AI and ML based decisions for the safe and acceptable use of these emerging technologies. Fairness, in particular, guarantees that the ML decisions do not result in discrimination against individuals or minorities. Identifying and measuring reliably fairness/discrimination is better achieved using causality which considers the causal relation, beyond mere association, between the sensitive attribute (e.g. gender, race, religion, etc.) and the decision (e.g. job hiring, loan granting, etc.). The big impediment to the use of causality to address fairness, however, is the unavailability of the causal model (typically represented as a causal graph). Existing causal approaches to fairness in the literature do not address this problem and assume that the causal model is available. In this paper, we do not make such assumption and we review the major algorithms to discover causal relations from observable data. This study focuses on causal discovery and its impact on fairness. In particular, we show how different causal discovery approaches may result in different causal models and, most importantly, how even slight differences between causal models can have significant impact on fairness/discrimination conclusions. These results are consolidated by empirical analysis using synthetic and standard fairness benchmark datasets. The main goal of this study is to highlight the importance of the causal discovery step to appropriately address fairness using causality.