On Discrimination Discovery and Removal in Ranked Data using Causal Graph
On Discrimination Discovery and Removal in Ranked Data using Causal Graph
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DOI：
10.1145/3219819.3220087
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发表时间：
2018-03
期刊：
影响因子：
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通讯作者：
Yongkai Wu;Lu Zhang;Xintao Wu
中科院分区：
文献类型：
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作者：
Yongkai Wu;Lu Zhang;Xintao Wu
从历史数据中学到的预测模型被广泛用于帮助公司和组织做出决策。但是，他们可能会在数字上不公平地对待不需要的群体，从而引起人们对公平和歧视的关注。在本文中，我们研究了公平感知的排名问题，该问题旨在发现排名的数据集中的歧视并重建公平排名。公平意识排名中的现有方法主要基于统计奇偶校验，由于歧视是因果关系，因此无法衡量真正的歧视效应。另一方面，基于因果关系的反歧视学习的现有方法专注于分类问题，不能直接应用于排名数据。为了解决这些限制，我们建议将等级位置映射到代表候选人资格的连续得分变量。然后，我们构建了一个因果图，该图形由离散配置文件属性和连续得分组成。路径特异性效应技术扩展到可混合变量的因果图，以识别直接和间接歧视。理论上分析了排名数据的路径特异性效应与二进制决策的关系之间的关系。最后，开发了用于发现和删除排名数据集歧视的算法。使用现实世界数据集的实验显示了我们方法的有效性。
Predictive models learned from historical data are widely used to help companies and organizations make decisions. However, they may digitally unfairly treat unwanted groups, raising concerns about fairness and discrimination. In this paper, we study the fairness-aware ranking problem which aims to discover discrimination in ranked datasets and reconstruct the fair ranking. Existing methods in fairness-aware ranking are mainly based on statistical parity that cannot measure the true discriminatory effect since discrimination is causal. On the other hand, existing methods in causal-based anti-discrimination learning focus on classification problems and cannot be directly applied to handle the ranked data. To address these limitations, we propose to map the rank position to a continuous score variable that represents the qualification of the candidates. Then, we build a causal graph that consists of both the discrete profile attributes and the continuous score. The path-specific effect technique is extended to the mixed-variable causal graph to identify both direct and indirect discrimination. The relationship between the path-specific effects for the ranked data and those for the binary decision is theoretically analyzed. Finally, algorithms for discovering and removing discrimination from a ranked dataset are developed. Experiments using the real-world dataset show the effectiveness of our approaches.