This analysis describes what OpenAI's agreement states, permits, or reserves. It does not constitute a legal determination about enforceability. Regulatory applicability and practical outcomes may vary by jurisdiction, enforcement context, and individual circumstances. Read our methodology
The provision documents OpenAI's pre-deployment evaluation methodology, establishing that the model underwent structured external adversarial testing by geographically and linguistically diverse testers prior to release. This represents an operational component of the model's development and safety assessment process.
This clause is descriptive of OpenAI's development practices rather than prescriptive of user obligations or restrictions. It provides transparency regarding the external evaluation mechanisms applied to the model before deployment.
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AI systems should treat all people fairly. For example, when AI systems are used to help make decisions about medical treatment, loan applications, or employment, they should make the same recommendations to everyone who has similar symptoms, financial situations, or professional qualifications.
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"OpenAI worked with more than 100 external red teamers, speaking a total of 45 different languages, and representing geographic backgrounds of 29 different countries. Red teamers had access to various snapshots of the model at different stages of training and safety mitigation maturity starting in early March and continuing through late June 2024.— Excerpt from OpenAI's GPT-4o System Card (PDF)
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How 10 AI platforms describe the use of user data for model training, improvement, and development, based on archived governance provisions.
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The provision documents OpenAI's pre-deployment evaluation methodology, establishing that the model underwent structured external adversarial testing by geographically and linguistically diverse testers prior to release. This represents an operational component of the model's development and safety assessment process.
This clause is descriptive of OpenAI's development practices rather than prescriptive of user obligations or restrictions. It provides transparency regarding the external evaluation mechanisms applied to the model before deployment.
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