This session is part of techUK's "Innovations in Physical AI" workshop series. 

Physical AI systems operating in safety-critical contexts — from autonomous vehicles to AI-guided surgical systems to industrial automation in high-risk environments — face fundamentally unique deployment challenges, especially when it comes to human safety. 

This session examines the practical realities of deploying physical AI in environments where safety is paramount. We'll explore what testing protocols, certification frameworks, and safety standards are emerging, how organisations are managing risk throughout the deployment lifecycle, and what the UK needs to build confidence in physical AI systems operating in safety-critical contexts. 

In this session, our discussion will likely focus on: 

  • Safety standards and testing protocols: What testing and validation approaches are being used for physical AI in safety-critical applications? How do organisations balance thorough safety testing with the need to iterate and improve systems? Are existing standards like ISO 26262 and ISO 21448 sufficient, or do we need new frameworks specifically designed for AI-enabled physical systems and world foundation models? 
  • Bridging simulation and reality in safety-critical testing: How are world foundation models and simulation environments being used to explore safety-critical scenarios that would be too dangerous or costly to test in the real world? What are the limitations of simulation-based approaches, and how can we combine virtual testing with structured mitigation and real-world evaluation to build confidence in safety outcomes? 
  • Interpretability and explainability in critical decisions: When physical AI systems make safety-critical decisions — whether to brake, route around an obstacle, or flag a medical concern — how do we ensure those decisions are interpretable and explainable? What does "legibility" mean for physical AI systems, and how do we design systems that can communicate their reasoning and intentions effectively? 
  • Risk management and liability frameworks: How should organisations structure accountability when physical AI systems operate with increasing autonomy in safety-critical environments? What insurance, liability, and legal frameworks are needed as these systems take actions that affect human safety? How do we balance innovation with appropriate safeguards and clear lines of responsibility? 

About the series 

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For more information about this series or to get involved, please contact the team below.

Kir Nuthi

Kir Nuthi

Head of AI and Data, techUK

Usman Ikhlaq

Usman Ikhlaq

Programme Manager - Artificial Intelligence, techUK



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Contact the team

Kir Nuthi

Kir Nuthi

Head of AI and Data, techUK

Usman Ikhlaq

Usman Ikhlaq

Programme Manager - Artificial Intelligence, techUK

Sue Daley OBE

Sue Daley OBE

Director, Technology and Innovation

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