  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.

Databases;Computers and Society;Machine Learning