An Operational Framework for Guiding Human Evaluation in Explainable and Trustworthy Artificial Intelligence

Abstract

The assessment of explanations by humans presents a significant challenge within the context of explainable and trustworthy artificial intelligence. This is attributed not only to the absence of universal metrics and standardized evaluation methods but also to the complexities tied to devising user studies that assess the perceived human comprehensibility of these explanations. To address this gap, we introduce a survey-based methodology for guiding the human evaluation of explanations. This approach amalgamates leading practices from existing literature and is implemented as an operational framework. This framework assists researchers throughout the evaluation process, encompassing hypothesis formulation, online user study implementation and deployment, and analysis and interpretation of collected data. The application of this framework is exemplified through two practical user studies.