AI safety beyond ‘solving alignment’ and model interpretability
A post argues that interpreting model internals has not produced the reliable, scalable understanding needed for formal safety guarantees. It calls for a broader approach drawn from disaster planning.
TLDR
The writer argues that AI systems will never be fully understood, so safety should not depend on solving alignment or interpretability. They propose imperfect forecasting, monitoring what systems actually do, limiting their power when confidence is low, building redundant protections and planning for those protections to fail.
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2 Sources, first seen 9h ago
