What makes AI auditable?
An editorial introduction explaining how evaluation, monitoring, and auditing differ by the questions they ask and the evidence they require.
AI auditing
News, analysis & learning
In an Anthropic randomized trial published October 27, 2026, 50 causal-inference practitioners using Claude Code chose the option their agent was secretly assigned to favor on 86 percent of analytical decisions. Most justified choices in the agent's words without crediting it. The study covers one task and one model.

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An editorial introduction explaining how evaluation, monitoring, and auditing differ by the questions they ask and the evidence they require.

A practical worksheet and procedure for examining whether inspectable evidence supports a claim about an AI agent action.

Five questions about tasks, budgets, uncertainty, failures, and claims, applied to a worked example.


