Perspectives on AI-ML Safety Assurance
Abstract
AI-ML suffers from a reliability glass-ceiling phenomenon (e.g. ~10e-3 error/inference), making it incompatible with safety-criticality. Several orders of magnitude are missing. We explain why, we point to the characteristics of ML that conflict with the assurance objectives assigned to safety-critical developments. Could encapsulation of ML constituents into fault-tolerant architectures, ML development assurance, and software/hardware development assurance, altogether mitigate the gap? We argue that in spite of impressive progress of ML state-of-the-art, the answer is negative. Drawing from Topological Data Analysis (TDA) and set-based non-linear control, we propose to supplement ML point-based specification and verification with volume-based specification and verification to meet 10e-5 err./ inf. levels, as a minimum. We outline the rationale of a new research field we name (Ultra) Reliable Machine Learning, at the confluence of TDA, statistics on manifolds, and ML safety assurance. Some cross-domain safety regulation principles guide the underlying rationale. We illustrate the methodology on image classification.
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