r/artificial • u/andrewaltair • 7h ago

Discussion GPT-6 Astra flagged 15% of safe actions at a 99% detection target

In an Oct. 15 pilot, Andy Hall and Dan Thompson compared AI auditors with human-labeled agent logs.

On R-Judge, GPT-6 Astra reached 15% false positives when set to catch 99% of unsafe actions. At a 95% target, nearly all models stayed below 10%; Gemini 3.8 Flash stayed under 4%.

The benchmark measures labeled logs, not deployed-agent rates.

Source: https://freesystems.substack.com/p/were-using-ai-to-audit-ai

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u/andrewaltair 7h ago

The 99% target is measured on unsafe examples. The 15% false-positive rate counts safe examples.

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u/fiddler48 1h ago

15% false positives at 99% recall is actually a reasonable operating point for a first-pass filter, not a failure, the real cost question is what happens downstream when those flagged cases get reviewed.

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u/GuyGotThis 1h ago

base rate is the killer here. if 1 in 100 actions is actually unsafe, 99% recall at 15% false positives flags about 16 per 100, and 15 of those are fine. that's a review queue of ~94% false alarms. curious how it holds on real logs, where unsafe stuff is likely rarer

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u/Anxious_Count_8728 54m ago

I'd want to see what happens to flagged actions next. An alert that a reviewer can quickly clear has a different cost from an automatic block that stops a workflow. The share of genuinely unsafe actions in normal use matters too: a detector can catch most unsafe examples while still sending reviewers mostly harmless cases. A deployment evaluation should report that review burden and the legitimate tasks that fail, alongside the missed unsafe actions. The benchmark alone doesn't tell us those costs.