What I Cannot Predict, I Do Not Understand: A Human-Centered Evaluation Framework for Explainability Methods.

ABSTRACT: A multitude of explainability methods has been described to try to help users better understand how modern AI systems make decisions. However, most performance metrics developed to evaluate these methods have remained largely theoretical - without much consideration for the human end-user. In particular, it is not yet clear (1) how useful current explainability methods are in real-world scenarios; and (2) whether current performance metrics accurately reflect the usefulness of explanation methods for the end user. To fill this gap, we conducted psychophysics experiments at scale (n = 1,150) to evaluate the usefulness of representative attribution methods in three real-world scenarios. Our results demonstrate that the degree to which individual attribution methods help human particip

SUBMITTER: Colin J 

PROVIDER: S-EPMC10544769 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature