Through the Fairness Lens: Experimental Analysis and Evaluation of Entity Matching
This addresses fairness issues in entity matching for applications involving social data, but it is incremental as it applies existing fairness concepts to EM without introducing new methods.
The paper tackles the problem of fairness in entity matching (EM) by experimentally evaluating various EM techniques on social datasets, finding potential unfairness under conditions like demographic overrepresentation and name similarity differences, with measures such as positive predictive value parity and true positive rate parity being more effective at revealing unfairness due to class imbalance.
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 overrepresented, 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.