Mechanistic Explanatory Strategy for XAI Outlined
A recent article published on arXiv introduces a mechanistic approach to explainable AI (XAI), utilizing insights from the philosophy of science to fill existing conceptual voids. This method focuses on uncovering the mechanisms that influence decision-making in deep learning models, including neurons, layers, circuits, and activation patterns, via decomposition, localization, and recomposition. Case studies that validate this concept are provided, showcasing applications in image recognition and language processing. Furthermore, this research places current advancements in XAI within a wider philosophical framework, striving to connect XAI with scientific discussions on the nature of explanation.
Key facts
- Paper outlines a mechanistic strategy for explaining deep learning systems
- Approach involves decomposition, localization, and recomposition of functional components
- Case studies from image recognition and language processing
- Situates XAI within philosophy of science literature
- Addresses lack of conceptual foundations in XAI
- Published on arXiv with ID 2411.01332v5
- Focuses on functional organization of deep neural networks
- Mechanisms include neurons, layers, circuits, or activation patterns
Entities
Institutions
- arXiv