Learning to Comprehend and Trust Artificial Intelligence Outcomes: A Conceptual Explainable AI Evaluation Framework

Abstract

Explainable artificial intelligence (XAI) is a burgeoning concept. It is gaining prominence as an approach to better understand how artificial intelligence solutions' outputs can improve decision making. Evaluation frameworks to enable organizations to understand XAIs what, why, how, and when are yet to be developed. Thus, we aim to fill this void by developing a conceptual content , context , process, and outcome (CCPO) evaluation framework to justify XAIs adoption and effective management using construction organizations as a backdrop for the article's setting. After introducing and describing the proposed novel CCPO framework for operationalizing XAI, we discuss its implications for future research. The contributions of our article are twofold: First, it highlights the need for organizations to embrace and enact XAI so that decision makers and stakeholders can better understand why and how a specific prediction materializes; and second, it provides a frame of reference for organizations to realize the business value and benefits of XAI.