OpenAI’s comment to the NTIA on open model weights
This comment was submitted by OpenAI in response to NTIA’s March 2024 Request for Information on Dual-Use Foundation Models with Widely Available Weights.
OpenAI believes that building, broadly deploying, and using AI can improve people’s lives and unlock a better future. Progress relies on innovation and free market competition. Within those broad guidelines, there are many different paths by which people can further the promise of AI. OpenAI was among the first AI developers to wrestle with the question of how to distribute the benefits of unprecedentedly-capable foundation models, and we start by providing this historical context to help inform the NTIA’s deliberations.
In 2019, we created GPT-2, which had the new capability of generating coherent paragraphs of text, and were faced with the question of how to deploy it. On the one hand, the model seemed very useful; on the other hand, we weren’t sure if it could be useful for malicious purposes such as phishing email generation. We opted to experiment with a “staged release”. As we wrote at the time, “staged release involves the gradual release of a family of models over time. The purpose of our staged release of GPT-2 is to give people time to assess the properties of these models, discuss their societal implications, and evaluate the impacts of release after each stage.” When we did not observe significant misuse effects, this gave us the confidence to openly release the full model weights.
In 2020, we created GPT-3, which was much more capable than any previous language model on every benchmark, and again faced the question of how to release it. This time, we decided to release it via our first product, the OpenAI API (an Application Programming Interface, which allows developers to build apps on our technology). As we wrote at the time, we had several motivations for this new release strategy: “commercializing the technology helps us pay for our ongoing AI research, safety, and policy efforts” and “the API model allows us to more easily respond to misuse of the technology. Since it is hard to predict the downstream use cases of our models, it feels inherently safer to release them via an API and broaden access over time, rather than release an open source model where access cannot be adjusted if it turns out to have harmful applications.” Over several years, this API release taught us and the community lessons about the safety and misuse patterns of GPT-3 level models.
