Governments around the world are pushing AI regulation that has nothing to say about generative models. That could be dangerous.
. The organizations deploying these models for a specific use case will ultimately determine whether they are suitable and meet the necessary performance and robustness requirements. However, transparency around the use of these foundational models is essential to making potential harms visible. Deployers must credit the foundational models involved, enabling users, auditors, and the broader community to evaluate the risks of these downstream applications..
More focus from both industry players and regulators is needed to scale and standardize notification mechanisms that would enable users to report false, biased, or harmful outputs created by foundational models. This can serve two important targets: helping further train foundational models with human feedback from downstream applications and providing researchers and regulators with real-world data to inform the development of risk mitigations and policies.
Recent weeks have offered a glimpse into the current state of AI capabilities, which are both fascinating and worrying. Regulators will need to adjust their thinking to address coming developments, and industry will need to collaborate with policymakers as we navigate the next AI paradigm shift.
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