Through the Fairness Lens: Experimental Analysis and Evaluation of Entity Matching
Through the Fairness Lens: Experimental Analysis and Evaluation of Entity Matching
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DOI：
10.14778/3611479.3611525
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发表时间：
2023-07
期刊：
影响因子：
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通讯作者：
N. Shahbazi;Nikola Danevski;F. Nargesian;Abolfazl Asudeh;D. Srivastava
中科院分区：
文献类型：
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作者：
N. Shahbazi;Nikola Danevski;F. Nargesian;Abolfazl Asudeh;D. Srivastava
实体匹配（Entity Matching，EM）是一个具有挑战性的问题，被不同的团体研究了半个多世纪。数学公平也成为解决机器偏见及其社会影响的及时话题。尽管对这两个主题进行了广泛的研究，但很少关注实体匹配的公平性。为了解决这一差距，我们在本文中进行了广泛的实验评估的各种EM技术。我们从公开的数据集中生成了两个社会数据集，目的是通过公平的透镜审计EM。我们的研究结果强调了现实社会中两种常见情况下的潜在不公平：（i）当某些人口群体被过度代表时，以及（ii）当某些群体中的姓名与其他群体相比更相似时。在我们的许多研究结果中，值得一提的是，虽然各种公平性定义对于不同的设置是有价值的，但由于EM的类别不平衡性质，一般来说，阳性预测值奇偶性和真阳性率奇偶性等指标更能揭示EM的不公平性。
Entity matching (EM) is a challenging problem studied by different communities for over half a century. Algorithmic fairness has also become a timely topic to address machine bias and its societal impacts. Despite extensive research on these two topics, little attention has been paid to the fairness of entity matching. Towards addressing this gap, we perform an extensive experimental evaluation of a variety of EM techniques in this paper. We generated two social datasets from publicly available datasets for the purpose of auditing EM through the lens of fairness. Our findings underscore potential unfairness under two common conditions in real-world societies: (i) when some demographic groups are over-represented, and (ii) when names are more similar in some groups compared to others. Among our many findings, it is noteworthy to mention that while various fairness definitions are valuable for different settings, due to EM's class imbalance nature, measures such as positive predictive value parity and true positive rate parity are, in general, more capable of revealing EM unfairness.