  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.
