The Royal Statistical Society’s AI Task Force has issued a critical mandate via a new paper, AI Regulation Needs Statistics, which demands that statistical principles actively shape global AI governance.
The publication escalates the core argument of their foundational work, AI is Statistics. This earlier paper argued that AI is fundamentally statistical, meaning effective and ethical deployment is impossible without statistical literacy. You can watch our expert panel discuss the topic here.
A major focus of that work was around the challenges of evaluating AIs, given that they are dynamic systems that continue to evolve once they have been deployed in the real world. The new paper looks at three sectors where AI is increasingly used – healthcare, education and finance – and shows some of the ways in which statistical evaluation would support regulation in those sectors. From those case studies it draws five key recommendations:
- Government should establish clear responsibility for identifying and addressing gaps in AI regulation
- Regulators should ensure that the evaluation of the AI systems deployed in their sector reflects how AIs are operating in practice.
- Government should ensure regulators have the right powers to require evidence generation for high-risk AI systems
- Regulators should build their statistical capability and embed statistical thinking within their organisations.
- Regulators should provide clear statistical guidance to organisations on the evaluation of AI to inform their procurement processes.
Following the paper’s publication, we caught up with Task Force Chair Donna Phillips. You can watch our conversation with her below:
Watch the interview
- About the author:
- Annie Flynn is Head of Content at the Royal Statistical Society.
Copyright and licence : © 2026 Annie Flynn
This article is licensed under a Creative Commons Attribution 4.0 (CC BY 4.0) International licence.