Intrinsic Barriers to Explaining Deep Foundation Models
It addresses the critical need for understanding DFMs to ensure trust, safety, and accountability in AI systems, highlighting potential intrinsic barriers.
The paper investigates whether the challenges in explaining Deep Foundation Models (DFMs) are temporary or intrinsic, examining their fundamental characteristics and the limitations of current explainability methods.
Deep Foundation Models (DFMs) offer unprecedented capabilities but their increasing complexity presents profound challenges to understanding their internal workings-a critical need for ensuring trust, safety, and accountability. As we grapple with explaining these systems, a fundamental question emerges: Are the difficulties we face merely temporary hurdles, awaiting more sophisticated analytical techniques, or do they stem from \emph{intrinsic barriers} deeply rooted in the nature of these large-scale models themselves? This paper delves into this critical question by examining the fundamental characteristics of DFMs and scrutinizing the limitations encountered by current explainability methods when confronted with this inherent challenge. We probe the feasibility of achieving satisfactory explanations and consider the implications for how we must approach the verification and governance of these powerful technologies.