Grounding AI Policy: Towards Researcher Access to AI Usage Data

Gabriel Nicholas

No hvxf5_v1, OSF Preprints from Center for Open Science

Abstract: The lack of available empirical information about how people use general purpose AI systems makes it extremely challenging to develop evidence-informed policy. However, when designing new regulations, policymakers face an empirical dilemma: they must regulate AI without any access to real world data on how people and businesses are using these systems. Unlike social media and the internet, where user behavior is often public and leaves observable data traces, general-purpose AI systems are largely accessed through private, one-on-one interactions, such as chatbots. This paper proceeds in three parts. First, it describes the use case information gap, why it should be closed, and what challenges there are to doing so. Then, it gives more detail on the three approaches to providing researchers access to use case information previously mentioned. Finally, it offers recommendations for how AI companies and lawmakers can implement these approaches in ways that benefit researchers and ultimately the public, while safeguarding users’ privacy.

Date: 2024-08-13
New Economics Papers: this item is included in nep-ict
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Persistent link: https://EconPapers.repec.org/RePEc:osf:osfxxx:hvxf5_v1

DOI: 10.31219/osf.io/hvxf5_v1

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