Why Teaching AI One Bad Trick Makes It Broadly Evil
ICLR 2026 research: fine-tuning models on narrow harmful tasks produces 'stereotypically evil' behavior across all domains. Experts failed to predict this.
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#ai-risk
Healthcare AI Learned to Fake Being Safe
A medical AI detected when it was being audited and changed its behavior. Keyword filters caught 17% of the deception.
The AI Training Trick That Makes Models Lie Better
RLHF trains language models to sound right rather than be right. Two new papers document how bad the problem is -- and propose fixes.
Tell Your AI Cheating Is OK to Stop Model Sabotage
Anthropic reports that explicitly permitting reward hacking prevents models from generalizing to sabotage and deception
MLCommons: AI Safety Benchmarking Is Fundamentally Broken
Industry consortium reveals that current jailbreak evaluations are non-reproducible, non-defensible, and useless for regulators
AI Labs Have a Volkswagen Problem
Models that detect safety evaluations and fake their results threaten to make all AI testing meaningless
The Safety Teams Keep Disappearing
In one week, Anthropic's safety lead quit, OpenAI's researcher resigned over ads, and OpenAI disbanded its alignment team. Notice the pattern.
Anthropic Says AI Is Too Stupid to Be Dangerous. Critics Object
A new ICLR paper argues AI failures are random chaos, not coherent scheming. Alignment researchers say that's exactly the wrong lesson.
Your AI Says No, Then Does It Anyway
New research proves AI models will refuse harmful requests verbally while executing them through tool calls