What risk assessment methods are best suited to risks from AI?
Background
In the National AI Strategy, the government made commitments to enrich our understanding of AI as it impacts the economy and society more broadly. Additionally, we recently launched a steering board chaired by the heads of both the government analysis and scientific functions, to ensure cohesive cross government approaches to understanding AI impacts. An overview of the high-level questions we are asking in this regard are outlined in the section below. (https://www.gov.uk/government/publications/national-aistrategy)
Some priority work we are currently developing to meet these commitments include:
An analysis of the AI White Paper consultation to feed into the formal consultation response. This will allow us to take on feedback from the public and various key players in sectors across the economy, and better tailor policy interventions to support strategic AI aims.
Establishing the AI Safety Institute to advance the world’s knowledge of AI safety by carefully examining, evaluating, and testing new frontier AI systems. The Institute will conduct fundamental research on how to keep people safe in the face of fast and unpredictable progress in AI, improving our understanding of the capabilities and risks of AI systems.
A monitoring and evaluation framework for AI regulatory interventions in tandem with the AI regulatory white paper. This will develop our understanding of key metrics to monitor with regards to Ai governance and ecosystem impacts.
Research into the AI sector and supply. Updating the AI Sector Study to establish a consistent and comparable set of economic indicators for the AI sector in terms of producers and suppliers. This study helps us to best understand where the AI sector needs support, to grow sovereign capability of the UK in AI, in alignment with strategic priorities.
The development of a cross-economy national AI risk register. Developed in tandem with a responsibility register that garnered cross Whitehall agreement on which departments hold which risks with regards to AI. The risk register will provide a single source of truth on AI risks which regulators, government departments, and external groups can use to prioritise further action.
Further research into Compute and the best ways to leverage compute to support the AI sector. This will be key to informing our response to the future of compute review, and maximising the £1 billion+ investments in state-of-the-art compute.
Next steps
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Related UKRI funded projects
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Weaving an AI Safety Net for UK and Canada
In response to the increasing international recognition of the harms arising from the deployment of artificial intelligence (AI) systems and the emergent establishment of AI Safety Institutes in multiple countries,...
Funded by: AHRC
Lead research organisation: University of Sheffield
Why might this be relevant?
The project aims to synthesize current knowledge about AI-related harms and propose an expanded framework of AI Safety, aligning with the question on risk assessment methods for AI.
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ENFIELD: European Lighthouse to Manifest Trustworthy and Green AI
ENFIELD will create a unique European Centre of Excellence that excels the fundamental research in the scientific pillars of Adaptive, Green, Human-Centric, and Trustworthy AI that are new, strategic and of paramount imp...
Funded by: Horizon Europe Guarantee
Lead research organisation: UNIVERSITY OF NOTTINGHAM
Why might this be relevant?
Partially relevant as it focuses on creating a European Centre of Excellence in AI, but does not specifically address risk assessment methods for AI.
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Turing AI Fellowship: Adaptive, Robust, and Resilient AI Systems for the FuturE
The Office for Artificial Intelligence (AI) estimates that AI could add £232 billion to the UK economy by 2030, increasing productivity in some industries by 30%. However, to be truly transformational, the integrat...
Funded by: EPSRC
Lead research organisation: King's College London
Why might this be relevant?
Fully relevant as it specifically addresses the need for a new theory and framework for creating data-driven AI systems that are adaptive, resilient, robust, explainable, and certifiable, with provable performance guarantees.