# Defining the scope of AI regulations

arXiv: https://arxiv.org/abs/1909.01095 · HTML: https://mlsanity.com/paper/1909.01095/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Aug 2019.

## Built on by (most cited first, 8 of 8)

- [Frontier AI Regulation: Managing Emerging Risks to Public Safety](https://mlsanity.com/paper/2307.03718/) (2023)
- [Conformity Assessments and Post-market Monitoring: A Guide to the Role of Auditing in the Proposed European AI Regulation](https://mlsanity.com/paper/2111.05071/) (2021)
- [Unlocking the Black Box: Analysing the EU Artificial Intelligence Act's Framework for Explainability in AI](https://mlsanity.com/paper/2502.14868/) (2025)
- [International Governance of Civilian AI: A Jurisdictional Certification Approach](https://mlsanity.com/paper/2308.15514/) (2023)
- [Closed-Loop View of the Regulation of AI: Equal Impact across Repeated Interactions](https://mlsanity.com/paper/2209.01410/) (2022)
- [Legal Infrastructure for Transformative AI Governance](https://mlsanity.com/paper/2602.01474/) (2026)
- [When Do Data-Driven Systems Exhibit the Capability to Infer?](https://mlsanity.com/paper/2606.11769/) (2026)
- [The Switch, the Ladder, and the Matrix: Models for Classifying AI Systems](https://mlsanity.com/paper/2407.05341/) (2024)

## Abstract (arXiv)

The paper argues that the material scope of AI regulations should not rely on the term "artificial intelligence (AI)". The argument is developed by proposing a number of requirements for legal definitions, surveying existing AI definitions, and then discussing the extent to which they meet the proposed requirements. It is shown that existing definitions of AI do not meet the most important requirements for legal definitions. Next, the paper argues that a risk-based approach would be preferable. Rather than using the term AI, policy makers should focus on the specific risks they want to reduce. It is shown that the requirements for legal definitions can be better met by defining the main sources of relevant risks: certain technical approaches (e.g. reinforcement learning), applications (e.g. facial recognition), and capabilities (e.g. the ability to physically interact with the environment). Finally, the paper discusses the extent to which this approach can also be applied to more advanced AI systems.