Explaining Expert Search and Team Formation Systems with ExES

TL;DR

ExES is an explainable AI tool that enhances transparency in expert search and team formation systems by providing factual and counterfactual explanations, significantly improving speed with pruning strategies.

Contribution

We introduce ExES, a novel explainability tool for expert search systems, incorporating efficient pruning strategies for practical, interactive deployment.

Findings

Pruning strategies make ExES up to ten times faster.

ExES provides concise, actionable explanations.

The tool improves transparency in expert identification processes.

Abstract

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.