Placing Practice and Expertise at the Center of Explainable AI: A Participatory Design Approach to Explainable AI for Enterprise Information Architects

AI presents significant opportunities to address long-standing challenges in Information Architecture (IA) practices within enterprises. However, research shows that shortcomings in the transparency and understandability of AI are hindering trust and limiting the adoption of effective solutions for Information Architecture. To overcome these barriers, this research places practice and experience at the center of Explainable AI (XAI) by collaborating with Information Management (IM) practitioners to develop an industry-based, context-aware approach to XAI based on their expertise. The paper outlines the participatory co-design development process implemented for the Explainable AI-to-EIM Lifecycle (XEL) workshop, enhancing the usefulness of AI applications for Enterprise Information Architecture. The research preliminary findings demonstrate how AI capabilities can be translated to meet practitioners’ needs, and by aligning the workshop's activities with high-level design aspects for XAI, the paper presents a human-centered XAI workshop design framework that is reusable, translatable, and transferable to other industry-based XAI contexts.