Integrating Evidence into the Design of XAI and AI-based Decision Support Systems: A Means-End Framework for End-users in Construction

Abstract: A narrative review is used to develop a theoretical evidence-based means-end framework to build an epistemic foundation to uphold explainable artificial intelligence instruments so that the reliability of outcomes generated from decision support systems can be assured and better explained to end-users. The implications of adopting an evidence-based approach to designing decision support systems in construction are discussed with emphasis placed on evaluating the strength, value, and utility of evidence needed to develop meaningful human explanations for end-users. While the developed means-end framework is focused on end-users, stakeholders can also utilize it to create meaningful human explanations. However, they will vary due to their different epistemic goals. Including evidence in the design and development of explainable artificial intelligence and decision support systems will improve decision-making effectiveness, enabling end-users' epistemic goals to be achieved. The proposed means-end framework is developed from a broad spectrum of literature. Thus, it is suggested that it can be used in construction and other engineering domains where there is a need to integrate evidence into the design of explainable artificial intelligence and decision support systems.