OpenAI releases LASER to find ambiguous AI safety cases
OpenAI has introduced LASER, a mechanism designed to identify rare and ambiguous conversations for AI safety evaluation. Released on October 6, 2026, the system addresses the difficulty of finding borderline cases in large datasets, where random sampling yields a disallowed rate of only about 1 in 20,000. In contrast, LASER selects conversations where roughly 1 in 2 are judged as disallowed,…
Key points
- LASER selects ambiguous conversations where the disallowed probability is around 50% for safety evaluation.
- The system reduces inference computational costs to approximately 1/10,000th compared to random sampling.
- LASER uses only synthetic and anonymized conversations, avoiding access to raw user data.
The process combines a lightweight logistic regression classifier with a high-performance inference model. It begins by generating synthetic and anonymized conversations, ensuring no raw user data is accessed. The inference model labels these conversations as allowed or disallowed based on safety policies. The classifier then learns from these labels to target conversations with a 50% probability of being disallowed, focusing on the ambiguous boundary where safety rules are most likely to fail.
LASER repeats this cycle of judging, updating the classifier, and re-extracting boundary cases until a diverse set of examples is collected. It uses greedy diversity sampling to ensure the evaluation set covers a wide range of situations. According to OpenAI, this method reduces the computational cost of inference by approximately 1/10,000th compared to random sampling while filtering data in a few hours. The tool is currently used to evaluate content related to violent illegal acts, self-harm, and sexual content.
How does AI safety testing find 'difficult conversations'? A gentle guide to OpenAI's 'LASER'
note.com · 24 October 2026
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