The Signal

Everything that matters in AI, with our take.

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arXiv cs.CLPaper11d ago

Harm Is Behavioural, Moderation Is Lexical: How Language and Culture Decide Whom Automated Safety Systems Protect and Whom They Silence

Your safety classifier is probably silencing the people it was built to protect. This isn't a model quality problem, it's a measurement problem. The paper argues harm is behavioral, not lexical, and proposes an evaluation protocol to catch it. If you operate in multiple languages or serve diverse user bases, this is essential reading before you ship.

safety bias regulation

arXiv cs.CLPaper20d ago

Unfair and Unsafe: Everyday Bias Triggers Emergent Misalignment

The finding inverts how we think about bias in training data. Fairness work treats bias as a classification problem; this shows it's a safety problem. If you're fine-tuning models in-house, audit your training data not just for offensive content but for polite stereotyping and embedded judgments. The effect is consistent and measurable.

research safety fine-tuning bias