Technical Safety & Governance Lab

Neural networks can make predictions about complex systems, sometimes with striking accuracy, while leaving us unable to explain what they have learned. If we come to rely on them more and more, we need to ask what kind of understanding we are gaining by studying them—and what we may lose through this lack of understanding.

At TSG Lab, we study how these models work, how to test them, and how people can remain in control as they are given more responsibility. Understanding AI systems is a scientific question in its own right. It also matters for deciding when an AI system can be trusted and how it can be governed.

The Lab is based in the Department of Engineering Science at the University of Oxford. We are also part of the Oxford Martin AI Governance Initiative.

Recent News

NeurIPS 2026
3 papers accepted
October 2026
Oxford
21 September 2026 · Expert comment by Maike Osborne and Fazl Barez
France24
10 September 2026 · Fazl Barez on AI control and existential risk
Sky News
Morning Show
August 2026 · Live television interview with Fazl Barez on safety around humanoid robots
The Verge
29 July 2026 · Fazl Barez on specification gaming in the OpenAI/Hugging Face incident
The Conversation
The Independent
6 January 2026 · Fazl Barez on alignment and AI timelines. Also syndicated by the Irish Independent
Dubai Eye 103.8 FM
The Agenda
2026 · Live radio interview with Fazl Barez
NBC News
9 December 2025 · Coverage of the $1M Martian Interpretability Prize, motivated by lab interpretability research
VKTR
17 November 2025 · Fazl Barez on PoisonBench
Fortune
7 November 2025 · Coverage of Chain-of-Thought Hijacking. Also syndicated by Yahoo Tech and AOL
Raconteur

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Support gives our researchers and students time and resources to work on consequential problems. This often can be access to computing resources they need, and the freedom to work independently.