🤖 AI Summary
This study addresses the challenge of “silent failures” in AI systems—errors that occur during training or deployment without manifesting as anomalies in loss functions, thereby evading conventional evaluation mechanisms. The work introduces the concept of “evaluation blind spots” and presents the first unified framework modeling silent failures across both training and deployment phases. It formally defines detectability predicates, establishes a six-category taxonomy of such failures, and proposes a risk-based failure budget framework. Through retrospective analysis of 50 real-world incidents, case studies, formal verification, and an open-source implementation featuring gradient tracing, log auditing, and contamination detection, the study reveals that 53% of publicly reported AI failures are silent in nature, identifies genuine gradient errors in the TRL library, and demonstrates that one of the six production failure categories is structurally silent in 100% of observed cases.
📝 Abstract
AI systems can fail silently. The failure propagates through training loops, evaluation pipelines, and production monitoring stacks until downstream harm makes it visible. This paper introduces evaluation blindness: a measurement function M exhibits evaluation blindness with respect to failure class F when it produces readings indistinguishable from a healthy state while the system is actually failing, with no auxiliary signal flagging the gap.
The problem surfaces at two lifecycle stages the literature has treated separately. At training time, reward models are gamed, importance-sampling corrections are silently miscalculated, and benchmark contamination inflates fine-tuning evaluations, all while loss curves look healthy and gradient updates proceed normally. At deployment time, monitoring fails to catch six classes of production failure, including an Operational category that is 100% silent by structural definition.
We provide a formal detectability predicate unifying both stages. Four training-time case studies trace concrete breakdowns, including a real implementation bug in TRL PR #6594 where gradients are corrupted as loss decreases normally. A six-class taxonomy validated against 50 real-world incidents from court documents and regulatory filings finds that 53% of verifiable public failures were silent. A failure budget framework ties acceptable failure rates to use-case risk class.
The implication is direct: measurement infrastructure is a correctness concern across the full AI lifecycle, not just at evaluation time. Data, code, and taxonomy schema are at https://github.com/priyanka25aug/llm-failure-taxonomy.