In this comment, we describe our thinking on AI accountability based on the safety practices we apply to the services we provide today, and the practices we plan to apply to services we anticipate offering in the future. We welcome NTIA’s decision to frame this discussion in terms of an “ecosystem” of AI accountability. As the RFC observes, policy stakeholders are exploring a “range of trustworthy AI system goals and deployment contexts.”1 Policies and practices designed to achieve accountability will vary accordingly. At the same time, specific accountability measures will need to coexist with one another, and what matters most is the impact they have in concert.

We believe that a mature ecosystem for AI accountability will include both horizontal and vertical elements. That is, we both expect there to be some elements that apply to certain AI systems across domains of application, as well as some elements that are tailored to particular domains. We are engaged in the development and deployment of highly capable foundation models—models that learn from a large amount of data in order to be able to perform a wide range of downstream tasks. In our view, AI developers like us must act responsibly and take a careful and safety-focused approach to the development and deployment of the most advanced capabilities. This is true regardless of the particular domains in which such models may be used.

A wide range of existing laws already apply to AI—including to our products—and the legal landscape is quickly evolving, with legislative initiatives in Congress, the AI Act under development in Europe, and legislative and policy initiatives unfolding around the world. At the same time, long-established bodies of law, regulation, and other expectations in areas like medicine, education, and employment are already being interpreted and adapted in ways that will shape the role AI plays in those domains. We see these sector-specific efforts, informed by deep domain expertise, as a critical part of the AI accountability landscape.

We strongly support efforts to harmonize the emergent accountability expectations for AI, including the efforts of the NIST AI Risk Management Framework, the U.S.-E.U. Trade and Technology Council, and a range of other global initiatives. While these efforts continue to progress, and even before new laws are fully implemented, we see a role for ourselves and other companies to make voluntary commitments on issues such as pre-deployment testing, content provenance, and trust and safety.