# Advancing Science- and Evidence-based AI Policy

arXiv: https://arxiv.org/abs/2508.02748 · HTML: https://mlsanity.com/paper/2508.02748/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Aug 2025.

## Built on by (most cited first, 7 of 7)

- [Measuring Mid-2025 LLM-Assistance on Novice Performance in Biology](https://mlsanity.com/paper/2602.16703/) (2026)
- [NeurIPS should lead scientific consensus on AI policy](https://mlsanity.com/paper/2510.00075/) (2025)
- [Internal Deployment Gaps in AI Regulation](https://mlsanity.com/paper/2601.08005/) (2026)
- [Algorithmic Monocultures in Hiring](https://mlsanity.com/paper/2605.27371/) (2026)
- [Paradox of De-identification: A Critique of HIPAA Safe Harbour in the Age of LLMs](https://mlsanity.com/paper/2602.08997/) (2026)
- [Governing Reflective Human-AI Collaboration: A Framework for Epistemic Scaffolding and Traceable Reasoning](https://mlsanity.com/paper/2604.14898/) (2026)
- [Reflexive Evidence-Based Multimodal Learning for Clean Energy Transitions: Causal Insights on Cooking Fuel Access, Urbanization, and Carbon Emissions](https://mlsanity.com/paper/2511.15342/) (2025)

## Abstract (arXiv)

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.