arXiv:2411.01332v5 Announce Type: replace Abstract: 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 research draws on explanatory strategies from various sciences and the 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 developments in explainable AI within a broader philosophical context. According to the mechanistic ap
The proliferation of complex AI systems necessitates more robust and philosophically grounded explainability frameworks to address issues of trust, ethics, and regulatory compliance.
A more solid conceptual foundation for XAI, integrating philosophy of science, will likely lead to more effective and trustworthy AI systems, impacting their adoption across critical sectors.
The approach to explainable AI shifts from purely technical solutions to incorporating mechanistic and philosophical perspectives, potentially leading to a more standardized and rigorous understanding of AI behavior.
- · AI ethicists
- · Regulatory bodies
- · Researchers in XAI
- · Industries requiring high-assurance AI
- · Developers of ad-hoc XAI solutions
- · Companies relying on opaque AI
Increased clarity and trustworthiness in deep learning systems.
Faster adoption of AI in sensitive applications due to enhanced interpretability and accountability.
The establishment of new interdisciplinary fields combining AI, philosophy, and cognitive science for system design.
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Read at arXiv cs.LG