The Role of Standards in Enabling the AI Stack

Abstract

The debate over AI standards is undergoing a significant reorientation.  In major jurisdictions, such as the United States, European Union, United Kingdom, and Australia, policymakers are increasingly shifting from a predominantly harm-centered approach toward one that emphasizes productivity, innovation, and the beneficial uses of AI while still managing risk.  At the same time, AI is increasingly being developed and deployed through a multi-party stack composed of data providers, foundation model developers, fine tuners, deployers, and other actors rather than by a single integrated firm.  In this environment, standards are essential not merely as a safety tool but as enabling infrastructure: they clarify roles, define interfaces, support validation, facilitate interoperability, and create the basis for ex post evaluation of real-world performance.  The Essay further contends that standards should generally emerge through flexible, multistakeholder processes and will likely vary across industry verticals rather than take the form of a single universal rule set.  It explores five core components of effective AI standards:  substantive performance requirements, meaningful data disclosures, transparent validation methods, protection against attacks, and articulation of acceptable levels of risk measured against real-world counterfactuals rather than zero-risk baselines.  Properly designed, such standards can unlock AI’s economic and social value while also mitigating bias, error, and safety concerns.