Advancing science- and evidence-based AI policy

Rishi Bommasani; Sanjeev Arora; Jennifer Chayes; Yejin Choi; Mariano Florentino Cuéllar; Li Fei-Fei; Daniel E. Ho; Dan Jurafsky; Sanmi Koyejo; Hima Lakkaraju; Arvind Narayanan; Alondra Nelson; Emma Pierson; Joelle Pineau; Scott Singer; Gaël Varoquaux; Suresh Venkatasubramanian; Ion Stoica; Percy Liang; Dawn Song

doi:10.1126/science.adu8449

Advancing science- and evidence-based AI policy

Rishi Bommasani
, Sanjeev Arora
, Jennifer Chayes
, Yejin Choi
, Mariano Florentino Cuéllar
, Li Fei-Fei
, Daniel E. Ho
, Dan Jurafsky
, Sanmi Koyejo
, Hima Lakkaraju
, Arvind Narayanan
, Alondra Nelson
, Emma Pierson
, Joelle Pineau
, Scott Singer
, Gaël Varoquaux
, Suresh Venkatasubramanian
, Ion Stoica
, Percy Liang
, Dawn Song

Research output: Contribution to journal › Review article › peer-review

1 Scopus citations

Abstract

Policy-makers around the world are grappling with how to govern increasingly powerful artificial intelligence (AI) technology. Some jurisdictions, like the European Union (EU), have made substantial progress enacting regulations to promote responsible AI. Others, like the administration of US President Donald Trump, have prioritized “enhancing America’s dominance in AI.” Although these approaches appear to diverge in their fundamental values and objectives, they share a crucial commonality: Effectively steering outcomes for and through AI will require thoughtful, evidence-based policy development (1). Though it may seem self-evident that evidence should inform policy, this is far from inevitable in the inherently messy policy process.

Original language	English (US)
Pages (from-to)	459-461
Number of pages	3
Journal	Science
Volume	389
Issue number	6759
DOIs	https://doi.org/10.1126/science.adu8449
State	Published - Jul 31 2025

All Science Journal Classification (ASJC) codes

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10.1126/science.adu8449

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Bommasani, R., Arora, S., Chayes, J., Choi, Y., Cuéllar, M. F., Fei-Fei, L., Ho, D. E., Jurafsky, D., Koyejo, S., Lakkaraju, H., Narayanan, A., Nelson, A., Pierson, E., Pineau, J., Singer, S., Varoquaux, G., Venkatasubramanian, S., Stoica, I., Liang, P., & Song, D. (2025). Advancing science- and evidence-based AI policy. Science, 389(6759), 459-461. https://doi.org/10.1126/science.adu8449

@article{2cbcc7b178d741e7b88abdda30b43c1a,

title = "Advancing science- and evidence-based AI policy",

abstract = "Policy-makers around the world are grappling with how to govern increasingly powerful artificial intelligence (AI) technology. Some jurisdictions, like the European Union (EU), have made substantial progress enacting regulations to promote responsible AI. Others, like the administration of US President Donald Trump, have prioritized “enhancing America{\textquoteright}s dominance in AI.” Although these approaches appear to diverge in their fundamental values and objectives, they share a crucial commonality: Effectively steering outcomes for and through AI will require thoughtful, evidence-based policy development (1). Though it may seem self-evident that evidence should inform policy, this is far from inevitable in the inherently messy policy process.",

author = "Rishi Bommasani and Sanjeev Arora and Jennifer Chayes and Yejin Choi and Cu{\'e}llar, \{Mariano Florentino\} and Li Fei-Fei and Ho, \{Daniel E.\} and Dan Jurafsky and Sanmi Koyejo and Hima Lakkaraju and Arvind Narayanan and Alondra Nelson and Emma Pierson and Joelle Pineau and Scott Singer and Ga{\"e}l Varoquaux and Suresh Venkatasubramanian and Ion Stoica and Percy Liang and Dawn Song",

year = "2025",

month = jul,

day = "31",

doi = "10.1126/science.adu8449",

language = "English (US)",

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Bommasani, R, Arora, S, Chayes, J, Choi, Y, Cuéllar, MF, Fei-Fei, L, Ho, DE, Jurafsky, D, Koyejo, S, Lakkaraju, H, Narayanan, A, Nelson, A, Pierson, E, Pineau, J, Singer, S, Varoquaux, G, Venkatasubramanian, S, Stoica, I, Liang, P & Song, D 2025, 'Advancing science- and evidence-based AI policy', Science, vol. 389, no. 6759, pp. 459-461. https://doi.org/10.1126/science.adu8449

TY - JOUR

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AU - Bommasani, Rishi

AU - Arora, Sanjeev

AU - Chayes, Jennifer

AU - Choi, Yejin

AU - Cuéllar, Mariano Florentino

AU - Fei-Fei, Li

AU - Ho, Daniel E.

AU - Jurafsky, Dan

AU - Koyejo, Sanmi

AU - Lakkaraju, Hima

AU - Narayanan, Arvind

AU - Nelson, Alondra

AU - Pierson, Emma

AU - Pineau, Joelle

AU - Singer, Scott

AU - Varoquaux, Gaël

AU - Venkatasubramanian, Suresh

AU - Stoica, Ion

AU - Liang, Percy

AU - Song, Dawn

PY - 2025/7/31

Y1 - 2025/7/31

N2 - Policy-makers around the world are grappling with how to govern increasingly powerful artificial intelligence (AI) technology. Some jurisdictions, like the European Union (EU), have made substantial progress enacting regulations to promote responsible AI. Others, like the administration of US President Donald Trump, have prioritized “enhancing America’s dominance in AI.” Although these approaches appear to diverge in their fundamental values and objectives, they share a crucial commonality: Effectively steering outcomes for and through AI will require thoughtful, evidence-based policy development (1). Though it may seem self-evident that evidence should inform policy, this is far from inevitable in the inherently messy policy process.

AB - Policy-makers around the world are grappling with how to govern increasingly powerful artificial intelligence (AI) technology. Some jurisdictions, like the European Union (EU), have made substantial progress enacting regulations to promote responsible AI. Others, like the administration of US President Donald Trump, have prioritized “enhancing America’s dominance in AI.” Although these approaches appear to diverge in their fundamental values and objectives, they share a crucial commonality: Effectively steering outcomes for and through AI will require thoughtful, evidence-based policy development (1). Though it may seem self-evident that evidence should inform policy, this is far from inevitable in the inherently messy policy process.

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SN - 0036-8075

VL - 389

SP - 459

EP - 461

JO - Science

JF - Science

IS - 6759

ER -

Advancing science- and evidence-based AI policy

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