The EU AI Act, Stakeholder Needs, and Explainable AI: Aligning Regulatory Compliance in a Clinical Decision Support System (2505.20311v1)

Abstract: Explainable AI (XAI) is a promising solution to ensure compliance with the EU AI Act, the first multi-national regulation for AI. XAI aims to enhance transparency and human oversight of AI systems, particularly ``black-box models'', which are criticized as incomprehensible. However, the discourse around the main stakeholders in the AI Act and XAI appears disconnected. While XAI prioritizes the end user's needs as the primary goal, the AI Act focuses on the obligations of the provider and deployer of the AI system. We aim to bridge this divide and provide guidance on how these two worlds are related. By fostering an interdisciplinary discussion in a cross-functional team with XAI, AI Act, legal, and requirements engineering experts, we walk through the steps necessary to analyze an AI-based clinical decision support system to clarify the end-user needs and assess AI Act applicability. By analyzing our justified understanding using an AI system under development as a case, we show that XAI techniques can fill a gap between stakeholder needs and the requirements of the AI Act. We look at the similarities and contrasts between the legal requirements and the needs of stakeholders. In doing so, we encourage researchers and practitioners from the XAI community to reflect on their role towards the AI Act by achieving a mutual understanding of the implications of XAI and the AI Act within different disciplines.