  Expert search and team formation systems operate on collaboration networks,
with nodes representing individuals, labeled with their skills, and edges
denoting collaboration relationships. Given a keyword query corresponding to
the desired skills, these systems identify experts that best match the query.
However, state-of-the-art solutions to this problem lack transparency. To
address this issue, we propose ExES, a tool designed to explain expert search
and team formation systems using factual and counterfactual methods from the
field of explainable artificial intelligence (XAI). ExES uses factual
explanations to highlight important skills and collaborations, and
counterfactual explanations to suggest new skills and collaborations to
increase the likelihood of being identified as an expert. Towards a practical
deployment as an interactive explanation tool, we present and experimentally
evaluate a suite of pruning strategies to speed up the explanation search. In
many cases, our pruning strategies make ExES an order of magnitude faster than
exhaustive search, while still producing concise and actionable explanations.
