How Microsoft Scales Testing and Safety for Generative AI with Sarah Bird - #691
Jul 1, 2024
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Join Sarah Bird, Chief Product Officer of Responsible AI at Microsoft, as she dives into the essential realms of generative AI testing and safety. Explore the challenges of AI hallucinations and the importance of balancing fairness with security. Hear insights from Microsoft's past failures like Tay and Bing Chat, stressing the need for adaptive testing and human oversight. Sarah also discusses innovative methods like automated safety testing and red teaming, emphasizing a robust governance framework for evolving AI technologies.
Implement layered defense for generative AI safety.
Manage risks with techniques like red teaming.
Prioritize security alongside fairness in AI applications.
Deep dives
Emphasizing Defense in Depth for Secure Systems
Starting with a system designed with defense in depth, where technologies are layered to counter weaknesses, resembles stacking Swiss cheese to prevent holes. Sarah Bird discusses building responsible AI applications, focusing on principles like fairness, transparency, accountability, and safety. The shift to generative AI requires new tools and techniques for implementation.
Identifying and Managing Risks for Generative AI
Risk identification and management for generative AI involves considering challenges like adversarial inputs, hallucinations, emissions, the potential to produce harmful content or IP material, and ensuring a clear user interface to prevent confusion or manipulation. Techniques like red teaming help assess risks and improve safety measures.
Balancing Fairness and Security Concerns
While fairness and bias remain essential in generative AI applications, the emphasis is shifting towards incorporating security measures due to concerns like hallucinations and adversarial attacks. Representational fairness is crucial in ensuring AI systems avoid stereotypes and maintain balanced representations.
Learning from Public AI Failure Cases
Reflecting on public AI failures, such as Microsoft's Tay and Bing Chat incidents, highlights lessons in responsible AI implementation. These cases underscore the importance of robust testing and evaluation processes, user feedback incorporation, and continuous monitoring to address potential risks.
Utilizing Testing Frameworks for AI Risk Management
Adopting frameworks like the NIST AI Risk Management Framework aids in structuring risk assessment, measurement, management, and governance for AI systems. Aligning testing and evaluation practices with risk assessments ensures a comprehensive approach to assessing and mitigating potential risks, such as harmful content generation.
Today, we're joined by Sarah Bird, chief product officer of responsible AI at Microsoft. We discuss the testing and evaluation techniques Microsoft applies to ensure safe deployment and use of generative AI, large language models, and image generation. In our conversation, we explore the unique risks and challenges presented by generative AI, the balance between fairness and security concerns, the application of adaptive and layered defense strategies for rapid response to unforeseen AI behaviors, the importance of automated AI safety testing and evaluation alongside human judgment, and the implementation of red teaming and governance. Sarah also shares learnings from Microsoft's ‘Tay’ and ‘Bing Chat’ incidents along with her thoughts on the rapidly evolving GenAI landscape.