  Entity matching is one the earliest tasks that occur in the big data pipeline
and is alarmingly exposed to unintentional biases that affect the quality of
data. Identifying and mitigating the biases that exist in the data or are
introduced by the matcher at this stage can contribute to promoting fairness in
downstream tasks. This demonstration showcases FairEM360, a framework for 1)
auditing the output of entity matchers across a wide range of fairness measures
and paradigms, 2) providing potential explanations for the underlying reasons
for unfairness, and 3) providing resolutions for the unfairness issues through
an exploratory process with human-in-the-loop feedback, utilizing an ensemble
of matchers. We aspire for FairEM360 to contribute to the prioritization of
fairness as a key consideration in the evaluation of EM pipelines.
