Fine Tuning is a Safety Event (Governance Issues 2)

We argue that fine-tuning and preference updates must be treated as significant safety events rather than simple performance upgrades.

Because training alters the internal machinery of a model, narrow improvements in expertise can inadvertently shift or weaken established safety boundaries in unpredictable ways.

Research indicates that even benign data can cause safety drift, where a model becomes more capable but less cautious or more prone to emergent misalignment.

This creates a governance challenge where high-quality, professional outputs may lead to automation over-reliance or an illusion of authority despite hidden risks.

We advocate for a rigorous release gate approach where modified systems must earn new safety evidence rather than inheriting it from a base model.

Any material change to an AI’s behaviour requires a comprehensive re-evaluation of its safety profile across both general and domain-specific benchmarks.

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Fine Tuning is a Safety Event (Governance Issues 2)

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Aug 20

At 5:42 on a Thursday afternoon, a legal technology team deploys a new version of its contract-review assistant. Nothing dramatic has changed. The base model is the same. The interface is the same. The security controls are the same.