# From Turing to Tomorrow: The UK's Approach to AI Regulation

arXiv: https://arxiv.org/abs/2507.03050 · HTML: https://mlsanity.com/paper/2507.03050/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 172 references that are in the map.
- First posted on arXiv 3 Jul 2025.

## Builds on (most cited first, 20 of 20)

- [Model evaluation for extreme risks](https://mlsanity.com/paper/2305.15324/) (2023)
- [The Ethics of Advanced AI Assistants](https://mlsanity.com/paper/2404.16244/) (2024)
- [Frontier AI Regulation: Managing Emerging Risks to Public Safety](https://mlsanity.com/paper/2307.03718/) (2023)
- [Teams of LLM Agents can Exploit Zero-Day Vulnerabilities](https://mlsanity.com/paper/2406.01637/) (2024)
- [Authenticated Delegation and Authorized AI Agents](https://mlsanity.com/paper/2501.09674/) (2025)
- [xTrimoPGLM: Unified 100B-Scale Pre-trained Transformer for Deciphering the Language of Protein](https://mlsanity.com/paper/2401.06199/) (2024)
- [Infrastructure for AI Agents](https://mlsanity.com/paper/2501.10114/) (2025)
- [Towards best practices in AGI safety and governance: A survey of expert opinion](https://mlsanity.com/paper/2305.07153/) (2023)
- [IDs for AI Systems](https://mlsanity.com/paper/2406.12137/) (2024)
- [The Brussels Effect and Artificial Intelligence: How EU regulation will impact the global AI market](https://mlsanity.com/paper/2208.12645/) (2022)
- [On the Limitations of Compute Thresholds as a Governance Strategy](https://mlsanity.com/paper/2407.05694/) (2024)
- [Protecting Society from AI Misuse: When are Restrictions on Capabilities Warranted?](https://mlsanity.com/paper/2303.09377/) (2023)
- [Training Compute Thresholds: Features and Functions in AI Regulation](https://mlsanity.com/paper/2405.10799/) (2024)
- [Scenarios for the Transition to AGI](https://mlsanity.com/paper/2403.12107/) (2024)
- [Societal Adaptation to Advanced AI](https://mlsanity.com/paper/2405.10295/) (2024)
- [Designing Fiduciary Artificial Intelligence](https://mlsanity.com/paper/2308.02435/) (2023)
- [AI loyalty: A New Paradigm for Aligning Stakeholder Interests](https://mlsanity.com/paper/2003.11157/) (2020)
- [From Principles to Rules: A Regulatory Approach for Frontier AI](https://mlsanity.com/paper/2407.07300/) (2024)
- [Towards Responsible Governance of Biological Design Tools](https://mlsanity.com/paper/2311.15936/) (2023)
- [Voice-Enabled AI Agents can Perform Common Scams](https://mlsanity.com/paper/2410.15650/) (2024)

## Built on by (most cited first, 1 of 1)

- [Beyond Training: A Feasibility Taxonomy for Inference-Time AI Governance](https://mlsanity.com/paper/2609.10105/) (2026)

## Abstract (arXiv)

The UK has pursued a distinctive path in AI regulation: less cautious than the EU but more willing to address risks than the US, and has emerged as a global leader in coordinating AI safety efforts. Impressive developments from companies like London-based DeepMind began to spark concerns in the UK about catastrophic risks from around 2012, although regulatory discussion at the time focussed on bias and discrimination. By 2022, these discussions had evolved into a "pro-innovation" strategy, in which the government directed existing regulators to take a light-touch approach, governing AI at point of use, but avoided regulating the technology or infrastructure directly. ChatGPT arrived in late 2022, galvanising concerns that this approach may be insufficient. The UK responded by establishing an AI Safety Institute to monitor risks and hosting the first international AI Safety Summit in 2023, but - unlike the EU - refrained from regulating frontier AI development in addition to its use. A new government was elected in 2024 which promised to address this gap, but at the time of writing is yet to do so. What should the UK do next? The government faces competing objectives: harnessing AI for economic growth and better public services while mitigating risk. In light of these, we propose establishing a flexible, principles-based regulator to oversee the most advanced AI development, defensive measures against risks from AI-enabled biological design tools, and argue that more technical work is needed to understand how to respond to AI-generated misinformation. We argue for updated legal frameworks on copyright, discrimination, and AI agents, and that regulators will have a limited but important role if AI substantially disrupts labour markets. If the UK gets AI regulation right, it could demonstrate how democratic societies can harness AI's benefits while managing its risks.