AI reasoning explanations fail four times in five: what to verify before shipping
The trajectory is encouraging — the most capable models performed best. But 20 percent is not a foundation for compliance frameworks.
AI models explain their reasoning all the time. Anthropic just measured how often those explanations are accurate: about 20 percent.
Their research tested whether Claude models can detect and report on their own internal neural states using a method called concept injection — planting known patterns into model activations and asking whether the model notices. The best models identified injected concepts roughly one in five tries. The other four? Missed signals, hallucinations, or incoherent outputs.
For product teams, this sets a hard constraint: any feature relying on model self-reporting needs independent validation, degradation protocols for the 80 percent failure case, and version-locked testing since introspection reliability varies across model generations.
For legal teams, the implications are sharper. If an AI system claims it excluded certain factors from a decision and that claim ends up in audit logs or regulatory filings, a 20 percent reliability rate means self-reports alone cannot satisfy explainability requirements. Pair every high-stakes self-report with an independent measurement and document your validation efforts.
The trajectory is encouraging — the most capable models performed best. But 20 percent is not a foundation for compliance frameworks. Verify first, ship second.
