A context-aware decision support system for selecting explainable artificial intelligence methods in business organizations

Abstract

Explainable Artificial Intelligence (XAI) methods are valuable tools for promoting understanding, trust, and efficient use of Artificial Intelligence (AI) systems in business organizations. However, the question of how organizations should select suitable XAI methods for a given task and business context remains a challenge, particularly when the number of methods available in the literature continues to increase. Here, we propose a context-aware decision support system (DSS) to select, from a given set of XAI methods, those with higher suitability to the needs of stakeholders operating in a given AI-based business problem. By including the human-in-the-loop, our DSS comprises an application-grounded analytical metric designed to facilitate the selection of XAI methods that align with the business stakeholders’ desiderata and promote a deeper understanding of the results generated by a given machine learning model. The proposed system was tested on a real supply chain demand problem, using real data and real users. The results provide evidence on the usefulness of our metric in selecting XAI methods based on the feedback and analytical maturity of stakeholders from the deployment context. We believe that our DSS is sufficiently flexible and understandable to be applied in a variety of business contexts, with stakeholders with varying degrees of AI literacy.