The safety failures we are not instrumenting: a perspective on hidden safety-critical challenges in modern AI systems
Current AI safety discourse still focuses disproportionately on visible failures, including obvious harms, dramatic misuse, and hypothetical catastrophic scenarios. That focus is incomplete. In deployed systems, many of the most consequential failures are quieter: plausible rather than spectacular, distributed across components rather than localized in a single output, and normalized by workflows before they are recognized as hazards. We argue that a central safety challenge in modern AI systems
Researchers argue that modern AI systems face hidden safety-critical challenges, including quieter failures that are plausible and distributed across components. A proposed five-layer framework identifies under-recognized risk patterns, such as overreliance and uncertainty laundering. The framework aims to shift AI safety from model-centric evaluation to socio-technical reliability.
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