AI policy

AI existential risk probabilities are too unreliable to inform policy

How speculation gets laundered through pseudo-quantification

Jul 26, 2024 •

Arvind Narayanan

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Tech policy is only frustrating 90% of the time

That’s what makes it worthwhile

Apr 3, 2024 •

Arvind Narayanan

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A safe harbor for AI evaluation and red teaming

An argument for legal and technical safe harbors for AI safety and trustworthiness research

Mar 5, 2024 •

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Arvind Narayanan

Generative AI’s end-run around copyright won’t be resolved by the courts

Output similarity is a distraction

Jan 22, 2024 •

Arvind Narayanan

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Are open foundation models actually more risky than closed ones?

A policy brief on open foundation models

Dec 15, 2023 •

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Arvind Narayanan

What the executive order means for openness in AI

Good news on paper, but the devil is in the details

Oct 31, 2023 •

Arvind Narayanan

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Generative AI companies must publish transparency reports

The debate about the harms of AI is happening in a data vacuum

Jun 26, 2023 •

Arvind Narayanan

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Three Ideas for Regulating Generative AI

Policy input to the federal government from a Stanford-Princeton team

Jun 21, 2023 •

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Arvind Narayanan

Licensing is neither feasible nor effective for addressing AI risks

Non-proliferation only benefits incumbents

Jun 10, 2023 •

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Arvind Narayanan

Artists can now opt out of generative AI. It’s not enough.

Opting out is the latest example of generative AI developers externalizing costs.

Mar 9, 2023 •

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Arvind Narayanan

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