SAFETY.md

AI safety rules, content filtering, and usage policies

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Recent SAFETY.md Documents

Validation-Based Refusal: A Transparent Alternative to Pre-emptive Pattern Matching in AI Safety Systems

Current AI safety architectures rely primarily on pre-emptive pattern matching to prevent harmful outputs, blocking requests based on surface-level indicators before evaluating actual intent. While effective at preventing certain attacks, this approach generates false positives that impede legitimate research and reduces transparency in safety decision-making. We present evidence that validation-based refusal architectures—which evaluate requests against explicit ethical axioms before deciding—c

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SAFETY.md