  Entity Matching (EM) is a critical task in numerous fields, such as
healthcare, finance, and public administration, as it identifies records that
refer to the same entity within or across different databases. EM faces
considerable challenges, particularly with false positives and negatives. These
are typically addressed by generating matching scores and apply thresholds to
balance false positives and negatives in various contexts. However, adjusting
these thresholds can affect the fairness of the outcomes, a critical factor
that remains largely overlooked in current fair EM research. The existing body
of research on fair EM tends to concentrate on static thresholds, neglecting
their critical impact on fairness. To address this, we introduce a new approach
in EM using recent metrics for evaluating biases in score based binary
classification, particularly through the lens of distributional parity. This
approach enables the application of various bias metrics like equalized odds,
equal opportunity, and demographic parity without depending on threshold
settings. Our experiments with leading matching methods reveal potential
biases, and by applying a calibration technique for EM scores using Wasserstein
barycenters, we not only mitigate these biases but also preserve accuracy
across real world datasets. This paper contributes to the field of fairness in
data cleaning, especially within EM, which is a central task in data cleaning,
by promoting a method for generating matching scores that reduce biases across
different thresholds.
