OpenAI has initiated the “OpenAI Red Teaming Network,” enlisting external experts to strengthen AI model risk evaluations and remedies. As AI, especially generative models, gains prominence, red teaming becomes crucial to pinpoint (but not necessarily rectify) biases seen in models like DALL-E 2 and oversights in text-generators like ChatGPT and GPT-4.
Previously collaborating with external experts for model assessments, OpenAI now formalizes this approach. This aims to enhance and expand partnerships with scientists, academic bodies, and civil groups, as stated in their blog.
OpenAI mentions, “This initiative aligns with third-party audits. Network members will contribute based on their specialization throughout the development phases.”
Members can also discuss general red teaming insights. Participation with each OpenAI release varies, and commitments may range from 5-10 times annually.
Inviting experts from diverse fields, OpenAI doesn’t mandate prior AI knowledge. However, there might be binding non-disclosure terms. OpenAI emphasizes, “Your engagement and perspective are vital. We welcome global applications, focusing on geographic and domain variety.”
Yet, is red teaming adequate? Some think not.
Aviv Ovadya of Harvard’s Berkman Klein Center suggests “violet teaming” – discerning potential harms of systems like GPT-4 and devising tools for protection. He emphasizes the lack of incentives for violet teaming and the rush in AI releases. Currently, OpenAI’s red teaming approach might be the optimum solution.