Explainability Auditing for Intelligent Systems: A Rationale for Multi-Disciplinary Perspectives
Abstract
National and international guidelines for trustworthy artificial intelligence (AI) consider explainability to be a central facet of trustworthy systems. This paper outlines a multi-disciplinary rationale for explainability auditing. Specifically, we propose that explainability auditing can ensure the quality of explainability of systems in applied contexts and can be the basis for certification as a means to communicate whether systems meet certain explainability standards and requirements. Moreover, we emphasize that explainability auditing needs to take a multi-disciplinary perspective, and we provide an overview of four perspectives (technical, psychological, ethical, legal) and their respective benefits with respect to explainability auditing.
Cite
@article{arxiv.2108.07711,
title = {Explainability Auditing for Intelligent Systems: A Rationale for Multi-Disciplinary Perspectives},
author = {Markus Langer and Kevin Baum and Kathrin Hartmann and Stefan Hessel and Timo Speith and Jonas Wahl},
journal= {arXiv preprint arXiv:2108.07711},
year = {2025}
}
Comments
Accepted at the First International Workshop on Requirements Engineering for Explainable Systems (RE4ES) co-located with the 29th IEEE International Requirements Engineering Conference (RE'21)