Ian Webster, founder and CEO of PromptFoo, shares his insights on AI safety and security, emphasizing the critical role of democratizing red teaming. He argues that open-source solutions can help identify vulnerabilities in AI applications, making security accessible to more organizations. The conversation also touches on lessons learned from Discord's early AI integration, the evolution of structured testing for more reliable AI, and the need for practical safeguards to tackle real-world risks rather than merely focusing on model size.
Democratizing red-teaming through open-source tools enables more developers to assess and improve AI safety at the application level.
Shifting regulatory focus from foundational models to practical use cases is essential for managing AI risks effectively in real-world scenarios.
Deep dives
The Ubiquity of AI and Its Associated Risks
AI is expected to become an essential tool in various applications, similar to databases, but this presents risks due to the potential for users to make poor decisions in its implementation. While banning AI might seem like a solution, it is impractical; instead, there should be a focus on establishing practical safeguards to manage those risks. Discussions emphasize that many issues arise not at the foundational model level but rather at the application layer, necessitating a shift in the focus of regulations. Addressing the interaction between models and their use cases is crucial to ensuring AI's safe deployment.
The Importance of Application Layer Evaluation
Historically, platforms like Discord have served as testing grounds for generative AI, exposing early developers to the intricacies of evaluating AI applications at scale. Developers faced challenges in adapting language models like GPT-3 for specific contexts, revealing that initial evaluations often focused on user engagement rather than safety. This led to the development of testing mechanisms like PromptFoo, which systematically assesses AI applications for safety and reliability, particularly in unexpected scenarios. The transition from subjective evaluations to structured, adversarial testing is essential for creating robust AI applications that can handle a diverse set of user interactions.
Red Teaming as a Tool for AI Safety
Red teaming emerges as a crucial evaluation method that simulates attempted breaches or misuse of AI models, revealing vulnerabilities in their design and implementation. The process involves generating malicious inputs to assess how AI applications may respond in adverse situations, highlighting areas that need improvement. Successful red teaming relies on the ability of developers to continuously measure and adjust their systems based on identified risks. Implementing structured red teaming allows organizations to enhance their AI safety defenses, particularly as instances of exploitation continue to rise.
The Need for Open Source Solutions in AI Security
The conversation around AI security is shifting towards the necessity for open-source tools that empower developers to tackle real-world risks rather than hypothetical scenarios. Many current AI regulations mistakenly focus on foundational models while failing to address the critical safety needs at the application level. Open-source initiatives can democratize access to security evaluations, enabling a wider range of developers to understand and mitigate their specific risks. To foster innovation responsibly, the development community needs collaborative approaches that prioritize security in practical environments.
In this episode of the AI + a16z podcast, a16z General Partner Anjney Midha speaks with PromptFoo founder and CEO Ian Webster about the importance of red-teaming for AI safety and security, and how bringing those capabilities to more organizations will lead to safer, more predictable generative AI applications. They also delve into lessons they learned about this during their time together as early large language model adopters at Discord, and why attempts to regulate AI should focus on applications and use cases rather than models themselves.
Here's an excerpt of Ian laying out his take on AI governance:
"The reason why I think that the future of AI safety is open source is that I think there's been a lot of high-level discussion about what AI safety is, and some of the existential threats, and all of these scenarios. But what I'm really hoping to do is focus the conversation on the here and now. Like, what are the harms and the safety and security issues that we see in the wild right now with AI? And the reality is that there's a very large set of practical security considerations that we should be thinking about.
"And the reason why I think that open source is really important here is because you have the large AI labs, which have the resources to employ specialized red teams and start to find these problems, but there are only, let's say, five big AI labs that are doing this. And the rest of us are left in the dark. So I think that it's not acceptable to just have safety in the domain of the foundation model labs, because I don't think that's an effective way to solve the real problems that we see today.
"So my stance here is that we really need open source solutions that are available to all developers and all companies and enterprises to identify and eliminate a lot of these real safety issues."