Adaptation of AI Explanations to Users' Roles

Julien Delaunay, Luis Galárraga, Christine Largouët, Niels van Berkel

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review


Surrogate explanations approximate a complex model by training a simpler model over an interpretable space. Among these simpler models, we identify three kinds of surrogate methods: (a) feature-attribution, (b) example-based, and (c) rule-based explanations. Each surrogate approximates the complex model differently, and we hypothesise that this can impact how users interpret the explanation. Despite the numerous calls for introducing explanations for all, no prior work has compared the impact of these surrogates on specific user roles (e.g., domain expert, developer). In this article, we outline a study design to assess the impact of these three surrogate techniques across different user roles.
Original languageEnglish
Title of host publicationAdjunct Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems - Workshop on Human-Centered Explainable AI
Number of pages7
Publication date2023
Publication statusPublished - 2023
Event2023 ACM CHI Conference on Human Factors in Computing Systems, CHI 23 -
Duration: 23 Apr 202328 Apr 2023


Conference2023 ACM CHI Conference on Human Factors in Computing Systems, CHI 23


Dive into the research topics of 'Adaptation of AI Explanations to Users' Roles'. Together they form a unique fingerprint.

Cite this