Data-Centric AI Governance: Addressing the Limitations of Model-Focused Policies

Ritwik Gupta, Leah Walker, Rodolfo Corona, Stephanie Fu, Suzanne Petryk, Janet Napolitano, Trevor Darrell, Andrew Reddie

Keywords: ai policy, data-centric ai

TL;DR: AI regulations affect us all, but their current form is ill-specified and overly broad. We introduce the importance of data into the conversation and provide alternatives for better AI policies that are more specific.

Abstract: Current regulations on powerful AI capabilities are narrowly focused on "foundation" or "frontier" models. However, these terms are vague and inconsistently defined, leading to an unstable foundation for governance efforts. Critically, policy debates often fail to consider the data used with these models, despite the clear link between data and model performance. Even (relatively) "small" models that fall outside the typical definitions of foundation and frontier models can achieve equivalent outcomes when exposed to sufficiently specific datasets. In this work, we illustrate the importance of considering dataset size and content as essential factors in assessing the risks posed by models both today and in the future. More broadly, we emphasize the risk posed by over-regulating reactively and provide a path towards careful, quantitative evaluation of capabilities that can lead to a simplified regulatory environment.

Primary Area: alignment, fairness, safety, privacy, and societal considerations

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Submission Number: 3234