A Conceptual Model for Implementing Explainable AI by Design: Results of an Empirical Study

Abstract

Artificial Intelligence (AI) offers organizations unprecedented opportunities. However, one of the risks of using AI is that its outcomes and inner workings are not intelligible. In industries where trust is critical, such as healthcare and finance, explainable AI (XAI) is a necessity. However, the implementation of XAI is not straightforward, as it requires addressing both technical and social aspects. Previous studies on XAI primarily focused on either technical or social aspects and lacked a practical perspective. This study aims to empirically examine the XAI related aspects faced by developers, users, and managers of AI systems during the development process of the AI system. To this end, a multiple case study was conducted in two Dutch financial services companies using four use cases. Our findings reveal a wide range of aspects that must be considered during XAI implementation, which we grouped and integrated into a conceptual model. This model helps practitioners to make informed decisions when developing XAI. We argue that the diversity of aspects to consider necessitates an XAI “by design” approach, especially in high-risk use cases in industries where the stakes are high such as finance, public services, and healthcare. As such, the conceptual model offers a taxonomy for method engineering of XAI related methods, techniques, and tools.