AI technologies have the potential to unlock significant growth for the UK financial services sector through novel personalised products and services, improved cost-efficiency, increased consumer confidence, and more effective management of financial, systemic, and security risks. However, there are currently significant barriers to adoption of these technologies, which stem from a capability deficit in translating high-level principles (of which there is an abundance) concerning trustworthy design, development and deployment of AI technologies ("trustworthy AI"), including safety, fairness, privacy-awareness, security, transparency, accountability, robustness and resilience, to concrete engineering, governance, and commercial practice. In developing an actionable framework for trustworthy AI, the major research challenge that needs to be overcome lies in resolving the tensions and tradeoffs which inevitably arise between all these aspects when considering specific application settings.For example, reducing systemic risk may require data sharing that creates security risks; testing algorithms for fairness may require gathering more sensitive personal data; increasing the accuracy of predictive models may pose threats to fair treatment of customers; improved transparency may open systems up to being "gamed" by adversarial actors, creating vulnerabilities to system-wide risks. This comes with a business challenge to match. Financial service providers that are adopting AI approaches will experience a profound transformation in key areas of business as customer engagement, risk, decisioning, compliance and other functions transition to largely data-driven and algorithmically mediated processes that involve less and less human oversight. Yet, adapting current innovation, governance, partnership and stakeholder relation management practice in response to these changes can only be successfully achieved once assurances can be confidently given regarding the trustworthiness of target AI applications. Our research hypothesis is based on recognising the close interplay between these research and business challenges: Notions of trustworthiness in AI can only be operationalised sufficiently to provide necessary assurances in a concrete business setting that generates specific requirements to drive fundamental research into practical solutions, with solutions which balance all of these potentially conflicting requirements simultaneously. Recognising the importance of close industry-academia collaboration to enable responsible innovation in this area, the partnership will embark on a systematic programme of industrially-driven interdisciplinary research, building on the strength of the existing Turing-HSBC partnership. It will achieve a step change in terms of the ability of financial service providers to enable trustworthy data-driven decision making while enhancing their resilience, accountability and operational robustness using AI by improving our understanding of sequential data-driven decision making, privacy- and security- enhancing technologies, methods to balance ethical, commercial, and regulatory requirements, the connection between micro- and macro-level risk, validation and certification methods for AI models, and synthetic data generation. To help drive innovation across the industry in a safe way which will help establish the appropriate regulatory and governance framework, and a common "sandbox" environment to enable experimentation with emerging solutions and to test their viability in a real-world business context. This will also provide the cornerstone for impact anticipation and continual stakeholder engagement in the spirit of responsible research and innovation.

However, access to silicon prototyping facilities remains a challenge in the UK due to the high cost of both equipment and the cleanroom facilities that are required to house the equipment. Furthermore, there is often a disconnect in communication between industry and academia, resulting in some industrial challenges remaining unsolved, and support, training, and networking opportunities for academics to engage with commercialisation activities isn't widespread. The C-PIC host institutions comprising University of Southampton, University of Glasgow and the Science and Technologies Facilities Council (STFC), together with 105 partners at proposal stage, will overcome these challenges by uniting leading UK entrepreneurs and researchers, together with a network of support to streamline the route to commercialisation, translating a wide range of technologies from research labs into industry, underpinned by the C-PIC silicon photonics prototyping foundry. Applications will cover data centre communications; sensing for healthcare, the environment & defence; quantum technologies; artificial intelligence; LiDAR; and more. We will deliver our vision by fulfilling these objectives: Translate a wide range of silicon photonics technologies from research labs into industry, supporting the creation of new companies & jobs, and subsequently social & economic impact. Interconnect the UK silicon photonics ecosystem, acting as the front door to UK expertise, including by launching an online Knowledge Hub. Fund a broad range of Innovation projects supporting industrial-academic collaborations aimed at solving real world industry problems, with the overarching goal of demonstrating high potential solutions in a variety of application areas. Embed equality, diversity, and inclusion best practice into everything we do. Deliver the world's only open source, fully flexible silicon photonics prototyping foundry based on industry-like technology, facilitating straightforward scale-up to commercial viability. Support entrepreneurs in their journey to commercialisation by facilitating networks with venture capitalists, mentors, training, and recruitment. Represent the interests of the community at large with policy makers and the public, becoming an internationally renowned Centre able to secure overseas investment and international partners. Act as a convening body for the field in the UK, becoming a hub of skills, knowledge, and networking opportunities, with regular events aimed at ensuring possibilities for advancing the field and delivering impact are fully exploited. Increase the number of skilled staff working in impact generating roles in the field of silicon photonics via a range of training events and company growth, whilst routinely seeking additional funding to expand training offerings.

Dynamic networks occur in many fields of science, technology and medicine, as well as everyday life. Understanding their behaviour has important applications. For example, whether it is to uncover serious crime on the dark web, intrusions in a computer network, or hijacks at global internet scales, better network anomaly detection tools are desperately needed in cyber-security. Characterising the network structure of multiple EEG time series recorded at different locations in the brain is critical for understanding neurological disorders and therapeutics development. Modelling dynamic networks is of great interest in transport applications, such as for preventing accidents on highways and predicting the influence of bad weather on train networks. Systematically identifying, attributing, and preventing misinformation online requires realistic models of information flow in social networks. Whilst simple random networks theory is well-established in maths and computer science, the recent explosion of dynamic network data has exposed a large gap in our ability to process real-life networks. Classical network models have led to a body of beautiful mathematical theory, but do not always capture the rich structure and temporal dynamics seen in real data, nor are they geared to answer practitioners' typical questions, e.g. relating to forecasting, anomaly detection or data ethics issues. Our NeST programme will develop robust, principled, yet computationally feasible ways of modelling dynamically changing networks and the statistical processes on them. Some aspects of these problems, such as quantifying the influence of policy interventions on the spread of misinformation or disease, require advances in probability theory. Dynamic network data are also notoriously difficult to analyse. At a computational level, the datasets are often very large and/or only available "on the stream". At a statistical level, they often come with important collection biases and missing data. Often, even understanding the data and how they may relate to the analysis goal can be challenging. Therefore, to tackle these research questions in a systematic way we need to bring probabilists, statisticians and application domain experts together. NeST's six-year programme will see probabilists and statisticians with theoretical, computational, machine learning and data science expertise, collaborate across six world-class institutes to conduct leading and impactful research. In different overlapping groups, we will tackle questions such as: How do we model data to capture the complex features and dynamics we observe in practice? How should we conduct exploratory data analysis or, to quote a famous statistician, "Looking at the data to see what it seems to say" (Tukey, 1977)? How can we forecast network data, or detect anomalies, changes, trends? To ground techniques in practice, our research will be informed and driven by challenges in many key scientific disciplines through frequent interaction with industrial & government partners in energy, cyber-security, the environment, finance, logistics, statistics, telecoms, transport, and biology. A valuable output of work will be high-quality, curated, dynamic network datasets from a broad range of application domains, which we will make publicly available in a repository for benchmarking, testing & reproducibility (responsible innovation), partly as a vehicle to foster new collaborations. We also have a strategy to disseminate knowledge through a diverse range of scientific publication routes, high-quality free software (e.g. R packages, Python notebooks accompanying data releases), conferences, patents and outreach activities. NeST will also carefully nurture and develop the next generation of highly-trained and research-active people in our area, which will contribute strongly to satisfying the high demand for such people in industry, government and academia.

The Northern Health Futures (NortHFutures) hub aims to create a world-leading healthcare technology (health-tech) development ecosystem. This will address unmet health needs and inequalities by supporting: inclusive digital skills training and sharing; research, innovation and entrepreneurship, enabled by digital design. Based in the North East and North Cumbria (NENC), with national and global reach, NortHFutures will support underserved communities, as it is known that national disparity of investment in NENC negatively impacts population health and wellbeing, and that a 'levelling up' of investment is needed to stimulate socio-economic and cultural growth for all, to encourage living and ageing well. NortHFutures builds upon the joined-up NENC approach to people-powered digital health innovation, as our regional Integrated Care Board (ICB) uniquely involves local authorities, communities, and citizens. Academic team members have a research track record that is stakeholder-involved and civic- and community-engaged. They are world-leading on understanding (i) health inequalities from medical, social, and design perspectives, and (ii) the opportunities for enrichment and enablement related to ageing well, connecting rural and urban populations, and pioneering applications of data science. In the pilot phase, we draw on this specialist expertise to address evidenced unmet health needs in NENC, (which have national and global importance): children and young people's health and nutrition; mental health and wellbeing; development of digital surgical pathways (for monitoring patient journeys beyond the hospital); living well with multiple long-term conditions. We combine the strengths and resources of 6 universities (Newcastle, Cumbria, Durham, Northumbria, Sunderland and Teesside), bringing regional investment in NIHR services, facilities and Applied Research Collaborations, plus National Innovation Centres for Ageing (NICA), Data (NICD) and Rural Enterprise (NICRE), National Horizons Centre (NHC), EPSRC Digital Economy programmes in data and digital citizens, and Health Data Research UK, the UK's national institute for health data science. NortHFutures supports new planned Centres, including Northumbria's Centre for Health & Social Equity and Cumbria's new campus and medical school. These University offers combine with an extensive partner network, including: ICB-NENC, 7 NHS Trusts, NHS Business Services Authority, Department of Health and Social Care, Health Education England; VCSE organisations delivering community-based services; industry partners - from SMEs to global tech giants; civic bodies such as Local and Combined Authorities; existing health research networks (e.g. AHSN-NENC, Newcastle Health Innovation Partnership); and innovation accelerators (e.g. Innovation SuperNetwork). Through an integrated, regional approach uniting this consortium for the first time, NortHFutures ambitiously aims to establish global leadership in Digital Health. To deliver this we will develop a supportive community infrastructure. We will co-design a digital brokerage service to connect and amplify partners' work, to offer and consume expertise, services and facilities (supporting acceleration of health-tech companies at differing tech-readiness levels). We will pioneer a Live Digital Health Databank, to explore, and train for, advanced healthcare data analytics, combining live data flows with care records (e.g. Great North Care Record). This will support personalised health diagnostics and interventions, giving our hub a unique value proposition to companies wishing to explore advanced data technologies. We will invest in Extended Reality pilots, to open up possibilities for clinical practice and service delivery. Our approaches will embed Responsible Research and Innovation (RRI), and Patient and Public Engagement (PPIE) throughout, to deliver health-tech that supports care beyond the hospital and is co-designed with end-users.

The ambition of this partnership between NATS and The Alan Turing Institute is to develop the fundamental science to deliver the world's first AI system to control a section of airspace in live trials. Our research will take a hierarchical approach to air traffic control (ATC) by developing a digital twin alongside a multi-agent machine-learning control system for UK airspace. Furthermore, the partnership will develop technical approaches to deploy trustworthy AI systems, considering how safety, explainability and ethics are embedded within our methods, so that we can deliver new tools which work in harmony with human air traffic controllers in a safety-critical environment. Little has changed in the fundamental infrastructure of UK airspace in the past 50 years, but demand for aviation has increased a hundredfold. Aviation 2050, a recent government green paper, underlines the importance of the aviation network to the prosperity of the UK to the value of £22 billion annually. Yet our nation is at risk without rapid action to modernise our airspace and control methods, to ensure they can handle a future increase in UK passenger traffic of over 50% by 2050 and new challenges arising from unmanned aircraft, both against a backdrop of increasing global pressures to transform the sector's environmental impact. The augmentation of live air traffic control through the use of AI agents which can handle the complexity and uncertainties in the system has transformative potential for NATS's business. This will positively impact live operations, as well as a research tool and training facility for new ATCOs. Correspondingly, NATS's research vision is to exploit new approaches to AI that enable increases in safety, capacity and environmental sustainability while streamlining air traffic controller training. The anticipated benefits of AI systems to air traffic control have come at a critical time, providing us with an opportunity to respond effectively to the unprecedented challenges which arise from a triad of crises: the coronavirus 2019 (Covid-19) pandemic, Brexit and global warming. The UK must develop independent technical advances in the sector, without compromising sustainability targets. The Alan Turing Institute is positioned at the rapidly evolving frontiers of probabilistic machine learning, safe and trustworthy AI and reproducible software engineering. Matching this with the world-leading expertise of NATS, supported by a world-first data set of more than 20 million flight records, means that this partnership is in a unique position to build the first multi AI agents system to deliver tactical control of UK airspace.