AI systems that learn on the job could add £232 billion to the UK economy by 2030, but only if they can be trusted not to fail in unpredictable ways. Current AI systems make decisions based on huge datasets, but they suffer from hidden biases, lack transparency, and cannot adapt to new situations without being retrained from scratch. This creates a dangerous gap: the more powerful AI becomes, the more it accumulates "technical debt" that makes it brittle and unreliable. The Office for AI estimates that fixing these flaws could boost productivity in some industries by 30%. This fellowship aims to build a new mathematical framework for AI that is certifiably robust, explainable, and able to learn continuously from human feedback without forgetting what it already knows. If successful, it would allow AI to be deployed safely in high-stakes settings like healthcare diagnosis, manufacturing quality control, satellite image analysis, and security screening—areas where a single mistake can have serious consequences. The work is primarily fundamental: it seeks to establish the theoretical limits of what large-scale AI systems can and cannot do, independent of any specific application. That deeper understanding will help practitioners set policy and prevent failures before they happen, rather than cleaning up after them.
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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 integration of AI throughout the global economy requires understanding and trust in the AI systems deployed. The super-human ability for decision-making in new AI systems requires huge volumes of data with thousands of variables, dependencies and uncertainties. Unregulated application of uncertified data-driven AI, limited by data bias and a lack of transparency, brings huge risks and necessitates a community-wide change. AI systems of the future must also be able to learn on-the-job to avoid becoming a high-interest credit card of huge technical debt. There is thus a timely and unmet need for a new theory and framework to enable the creation and analysis of data-driven AI systems that are adaptive, resilient, robust, explainable, and certifiable, with provable and practically relevant performance guarantees. This ambitious fellowship, ARaISE, will deliver a radically new framework for the creation of beneficial data-driven AI systems advancing far beyond classical theories by including certifiable robustness and learning in the problem setting. These new theories will enable a formal understanding of the fundamental limits of large-scale data-driven AI, independent of the application area and learning algorithms. This will enable AI practitioners, through understanding such limitations, to influence policy and prevent incidents before they occur. By connecting different and disparate areas of AI and Machine Learning, working with a world-class team of experts, and by engaging with stakeholders across strategic UK industries and sectors (Healthcare, Manufacturing, Space and Earth Observation, Smart Materials, and Security), ARaISE will create high-value, trustworthy, transformative and responsible AI, capable of reliably 'learning on-the-job' from humans to guarantee capability and trust. Novel human-centric AI, designed to function for the benefit of society, will complement and connect to existing work in the AI research arena, enabling co-development with project partners and focus on strategic industry challenges to ensure real-world relevance is built into research programme and its outputs, facilitating capacity and capability growth. ARaISE will generate gold standard tools for tasks that are currently heavily reliant upon human input and will support long-term global transformation. Impact and knowledge exchange activities, embedded throughout this programme of work, will support uptake of developed novel AI systems and, through leadership and ambassadorial activities, will support a step-change in how AI systems are built and maintained to ensure resilient, robust, adaptive and trustworthy operation. The inclusive research programme has been designed to support the career development of the project team and wider stakeholder group maximising the potential for flexible career paths whilst maintaining flexibility to creatively support the team to develop exciting new technology with real world relevance and guide future AI research. The issues of AI and ethics underpin the programme with responsible research and innovation embedded throughout its activities. Raising public and AI practitioners' awareness, and ultimately influencing policy by active engagement with the UK and AI ethics expertise and policymakers, will ensure that the outcomes are socially beneficial, ethical, trusted and deployable in real world situations. Planned engagement with the ATI, CDTs, partners, and their networks, the development of new partnerships, methodologies and applications, will encourage links between these organisations, build UK expertise, skills and capacity in AI and contribute to realising government investment in UK Societal Challenges and ensure that the UK remains at the forefront of the AI revolution.
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