# Deciding how to respond: A deliberative framework to guide policymaker responses to AI systems

arXiv: https://arxiv.org/abs/2508.03666 · HTML: https://mlsanity.com/paper/2508.03666/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 78 references that are in the map.
- First posted on arXiv 5 Aug 2025.

## Builds on (most cited first, 8 of 8)

- [Sociotechnical Safety Evaluation of Generative AI Systems](https://mlsanity.com/paper/2310.11986/) (2023)
- [The role of artificial intelligence in achieving the Sustainable Development Goals](https://mlsanity.com/paper/1905.00501/) (2019)
- [Frontier AI Regulation: Managing Emerging Risks to Public Safety](https://mlsanity.com/paper/2307.03718/) (2023)
- [How Knowledge Workers Think Generative AI Will (Not) Transform Their Industries](https://mlsanity.com/paper/2310.06778/) (2023)
- [Principles to Practices for Responsible AI: Closing the Gap](https://mlsanity.com/paper/2006.04707/) (2020)
- [Responsible Artificial Intelligence: A Structured Literature Review](https://mlsanity.com/paper/2403.06910/) (2024)
- [Comparative Global AI Regulation: Policy Perspectives from the EU, China, and the US](https://mlsanity.com/paper/2410.21279/) (2024)
- [Responsible AI Governance: A Systematic Literature Review](https://mlsanity.com/paper/2401.10896/) (2023)

## Abstract (arXiv)

The discourse on responsible artificial intelligence (AI) regulation is understandably dominated by risk-focused assessments and analyses. This approach reflects the fundamental uncertainty policymakers face when determining appropriate responses to current, emerging and novel AI systems. In this article, we argue that by operationalising the concept of freedom - the philosophical counterpart to responsibility - a complementary approach centred on the potential societal benefits of AI systems can be developed. The result is a discursive framework grounded in freedom as capability and freedom as opportunity, which represent the two main intellectual traditions of interpreting freedom. We contend that the complexity, ambiguity and contestation involved in regulating AI systems make a deliberative paradigm more useful than the conventional technical one. The resulting framework is structured around coordinative, communicative and decision spaces, each with sequential focal points and associated outputs.