  Entity resolution (ER) aims at matching records that refer to the same
real-world entity. Although widely studied for the last 50 years, ER still
represents a challenging data management problem, and several recent works have
started to investigate the opportunity of applying deep learning (DL)
techniques to solve this problem. In this paper, we study the fundamental
problem of explainability of the DL solution for ER. Understanding the matching
predictions of an ER solution is indeed crucial to assess the trustworthiness
of the DL model and to discover its biases. We treat the DL model as a black
box classifier and - while previous approaches to provide explanations for DL
predictions are agnostic to the classification task. we propose the CERTA
approach that is aware of the semantics of the ER problem. Our approach
produces both saliency explanations, which associate each attribute with a
saliency score, and counterfactual explanations, which provide examples of
values that can flip the prediction. CERTA builds on a probabilistic framework
that aims at computing the explanations evaluating the outcomes produced by
using perturbed copies of the input records. We experimentally evaluate CERTA's
explanations of state-of-the-art ER solutions based on DL models using publicly
available datasets, and demonstrate the effectiveness of CERTA over recently
proposed methods for this problem.
