A Mechanistic Explanatory Strategy for XAI

Despite significant advancements in XAI, scholars note a persistent lack of solid conceptual foundations and integration with broader scientific discourse on explanation. In response, emerging XAI research draws on explanatory strategies from various sciences and philosophy of science literature to fill these gaps. This paper outlines a mechanistic strategy for explaining the functional organization of deep learning systems, situating recent advancements in AI explainability within a broader philosophical context. According to the mechanistic approach, the explanation of opaque AI systems involves identifying mechanisms that drive decision-making. For deep neural networks, this means discerning functionally relevant components -- such as neurons, layers, circuits, or activation patterns -- and understanding their roles through decomposition, localization, and recomposition. Proof-of-principle case studies from image recognition and language modeling align these theoretical approaches with the latest research from AI labs like OpenAI and Anthropic. This research suggests that a systematic approach to studying model organization can reveal elements that simpler (or ''more modest'') explainability techniques might miss, fostering more thoroughly explainable AI. The paper concludes with a discussion on the epistemic relevance of the mechanistic approach positioned in the context of selected philosophical debates on XAI.