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.
|Title of host publication||Adjunct Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems - Workshop on Human-Centered Explainable AI|
|Number of pages||7|
|Publication status||Published - 2023|
|Event||2023 ACM CHI Conference on Human Factors in Computing Systems, CHI 23 - |
Duration: 23 Apr 2023 → 28 Apr 2023
|Conference||2023 ACM CHI Conference on Human Factors in Computing Systems, CHI 23|
|Period||23/04/2023 → 28/04/2023|