The safety failures we are not instrumenting: a perspective on hidden safety-critical challenges in modern AI systems
A new preprint contends that current AI safety discussions focus too narrowly on visible failures, overlooking subtler but significant risks in real-world deployments. The authors introduce a five-layer framework—covering epistemic, control, temporal, organizational, and ecosystem integrity—to identify and analyze hidden challenges such as overreliance, prompt injection, and model collapse. They argue for a shift from model-centric evaluation to a broader socio-technical reliability perspective.
Why it matters: This work reframes AI safety as a systemic issue, highlighting the need to address less visible but potentially more consequential risks in deployed AI systems.
Full story at: arXiv Computers and Society ↗