Explainable Information Retrieval: A Survey

TL;DR

This survey reviews recent methods for making information retrieval systems transparent and trustworthy, emphasizing explainability techniques, evaluation challenges, and future research directions in the context of complex machine learning models.

Contribution

It categorizes and unifies explainability methods across information retrieval domains, addressing evaluation issues and highlighting open challenges.

Findings

Provides a comprehensive framework for explainability in IR

Identifies key challenges in explanation evaluation

Highlights future opportunities in explainable IR

Abstract

Explainable information retrieval is an emerging research area aiming to make transparent and trustworthy information retrieval systems. Given the increasing use of complex machine learning models in search systems, explainability is essential in building and auditing responsible information retrieval models. This survey fills a vital gap in the otherwise topically diverse literature of explainable information retrieval. It categorizes and discusses recent explainability methods developed for different application domains in information retrieval, providing a common framework and unifying perspectives. In addition, it reflects on the common concern of evaluating explanations and highlights open challenges and opportunities.