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| Main Authors: | , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.02748 |
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| _version_ | 1866908478519377920 |
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| author | Bommasani, Rishi Arora, Sanjeev Chayes, Jennifer Choi, Yejin Cuéllar, Mariano-Florentino Fei-Fei, Li Ho, Daniel E. Jurafsky, Dan Koyejo, Sanmi Lakkaraju, Hima Narayanan, Arvind Nelson, Alondra Pierson, Emma Pineau, Joelle Singer, Scott Varoquaux, Gaël Venkatasubramanian, Suresh Stoica, Ion Liang, Percy Song, Dawn |
| author_facet | Bommasani, Rishi Arora, Sanjeev Chayes, Jennifer Choi, Yejin Cuéllar, Mariano-Florentino Fei-Fei, Li Ho, Daniel E. Jurafsky, Dan Koyejo, Sanmi Lakkaraju, Hima Narayanan, Arvind Nelson, Alondra Pierson, Emma Pineau, Joelle Singer, Scott Varoquaux, Gaël Venkatasubramanian, Suresh Stoica, Ion Liang, Percy Song, Dawn |
| contents | AI policy should advance AI innovation by ensuring that its potential benefits are responsibly realized and widely shared. To achieve this, AI policymaking should place a premium on evidence: Scientific understanding and systematic analysis should inform policy, and policy should accelerate evidence generation. But policy outcomes reflect institutional constraints, political dynamics, electoral pressures, stakeholder interests, media environment, economic considerations, cultural contexts, and leadership perspectives. Adding to this complexity is the reality that the broad reach of AI may mean that evidence and policy are misaligned: Although some evidence and policy squarely address AI, much more partially intersects with AI. Well-designed policy should integrate evidence that reflects scientific understanding rather than hype. An increasing number of efforts address this problem by often either (i) contributing research into the risks of AI and their effective mitigation or (ii) advocating for policy to address these risks. This paper tackles the hard problem of how to optimize the relationship between evidence and policy to address the opportunities and challenges of increasingly powerful AI. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_02748 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Advancing Science- and Evidence-based AI Policy Bommasani, Rishi Arora, Sanjeev Chayes, Jennifer Choi, Yejin Cuéllar, Mariano-Florentino Fei-Fei, Li Ho, Daniel E. Jurafsky, Dan Koyejo, Sanmi Lakkaraju, Hima Narayanan, Arvind Nelson, Alondra Pierson, Emma Pineau, Joelle Singer, Scott Varoquaux, Gaël Venkatasubramanian, Suresh Stoica, Ion Liang, Percy Song, Dawn Computers and Society AI policy should advance AI innovation by ensuring that its potential benefits are responsibly realized and widely shared. To achieve this, AI policymaking should place a premium on evidence: Scientific understanding and systematic analysis should inform policy, and policy should accelerate evidence generation. But policy outcomes reflect institutional constraints, political dynamics, electoral pressures, stakeholder interests, media environment, economic considerations, cultural contexts, and leadership perspectives. Adding to this complexity is the reality that the broad reach of AI may mean that evidence and policy are misaligned: Although some evidence and policy squarely address AI, much more partially intersects with AI. Well-designed policy should integrate evidence that reflects scientific understanding rather than hype. An increasing number of efforts address this problem by often either (i) contributing research into the risks of AI and their effective mitigation or (ii) advocating for policy to address these risks. This paper tackles the hard problem of how to optimize the relationship between evidence and policy to address the opportunities and challenges of increasingly powerful AI. |
| title | Advancing Science- and Evidence-based AI Policy |
| topic | Computers and Society |
| url | https://arxiv.org/abs/2508.02748 |