Title

AWS re:Invent 2023 - Responsible AI in the generative era: Science and practice (AIM220)

Summary

Michael Kearns, an Amazon scholar, and Peter, a leader of a central team at AWS, discuss the challenges and practices of responsible AI in the generative era.
Kearns highlights the scientific advancements and challenges in responsible AI, particularly with generative AI's open-ended output.
He discusses the difficulties in defining and enforcing fairness, privacy, and other concerns in large language models (LLMs).
Peter outlines AWS's approach to embedding responsible AI practices in ML solutions, emphasizing the importance of narrow use cases, risk-based approaches, treating datasets as specs, and operationalizing shared responsibility.
AWS AI service cards, tools on SageMaker, and investments in science and practice are mentioned as part of AWS's efforts to advance responsible AI.
The talk concludes with a call to action for organizations to build awareness, establish foundational skills, and participate in policy and regulatory discussions.

Generative AI's open-endedness introduces new complexities in responsible AI, making it harder to define and enforce fairness and privacy.
Kearns suggests that the science behind responsible AI in the pre-generative era does not suffice for the challenges posed by generative AI, indicating a need for new approaches and solutions.
Peter emphasizes the importance of defining application use cases narrowly to avoid broad risk analyses that could hinder development.
A risk-based approach to AI development is crucial, considering the potential impact on various stakeholders, not just the organization.
Treating datasets as product specs is essential, as they encode design policies and influence the performance of AI applications.
The shared responsibility model between providers and users of AI models is highlighted, suggesting that both parties must actively participate in risk assessment, data set creation, and feedback loops.
AWS's investment in tools, frameworks, and education around responsible AI indicates a commitment to addressing these challenges and supporting stakeholders in the AI ecosystem.
The talk underscores the importance of product managers in responsible AI, as they are central to balancing trade-offs between product features, costs, and responsible AI properties.
Participation in policy, legislative, and regulatory discussions is encouraged to ensure that standards and regulations are effective and informed by practical experience.