The last decade has seen a proliferation of social data that betrays human behaviour--from a person's clicks to a group's language to a state's signal data. Concurrently there has been rapid developments in our ability to analyse these data, to identify patterns that enable inferences not conceivable before. These advances open exciting possibilities for understanding human behaviour at the individual, group and population level. But they are also a source of insecurity and vulnerability, challenging the meaning of privacy and exposing individuals to manipulation. NABS+ will deliver 'next generation behavioural science' that addresses these opportunities and threats in the context of security and defence. It embraces new forms of data and analytics for behavioural science, while acknowledging the need to simultaneously address ethical and societal consequences. It will promote a generation of 'analytical behavioural scientists' who are equipped to respond to existential and acute threats, can understand and navigate policy dilemmas, and are adept at working with diverse data. NABS+ will be home to a vibrant community of researchers and end-users who recognise the value of combining social scientific theory and novel data analytics. Our vision is to stimulate an interdisciplinary community that is: (1) focused on identifying, prioritising and developing knowledge in analytical behavioural science through a theory-led, data-driven lens; (2) structured to be agile and responsive to international security trends and stakeholder needs, recognising that diverse data and methods are needed for different challenges; and (3) driven to create a community that is home to an emerging generation of data literate, behavioural scientists. NABS+ will deliver four pillars of activity. The Community pillar will deliver activities that shape new forms of public-private partnerships, connecting academic, industry, and government stakeholders to deliver challenge-driven collaborations. NABS+ is premised on the value of bringing together behavioural and data scientists. It thus invests in outputs that break down traditional groupings and increasing awareness (e.g., white papers, website). The Research pillar will co-develop and deliver interdisciplinary research that responds to immediate needs and prepares for future threats. The work may be an event, a synthetic review, or novel research, and we strive for a diversity of data and methodological approaches. In all cases, however, projects will be theory-led, data-driven activities that consider ethical and society implications at all stages. The Disseminate pillar recognises that progress in analytical behavioural science will depend on researchers understanding the activities and concepts of others. The pillar will deliver world-class activities that 'translate' knowledge so that understanding spreads beyond specialist audiences. As part of this, NABS+ will support researchers inform policy and practice, utilising unique Research to Practice Fellow support. The Build pillar seeks to ensure growth in the NABS+ community so that it becomes home to a next generation of behavioural scientist. The pillar involves activities that foster the skills and leadership of members through training and by providing secondment opportunities (e.g., academic researchers into industry). As part of this, NABS+ will implement a programme of actions designed to progress the network toward being self-supporting. The cumulative impact of these activities will be a world-class analytical behavioural science capability; a vibrant community with shared understanding, common tools, and a programme of ongoing research that shapes understanding and contributes profoundly to policy and practice.

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.

The proposed programme of research will establish the machine learning foundations and artificial intelligence methodologies for Digital Twins. Digital Twins are digital representations of real-world physical phenomena and assets, that are coupled with the corresponding physical twin through instrumentation and live data and information flows. This research programme will establish next-generation Digital Twins that will enable decision makers to perform accurate but simulated "what-if" scenarios in order to better understand the real world phenomena and improve overall decision making and outcomes.

(written with PPI panel) Many aspects of a young person's life can affect their mental health(MH), and there is a crisis in our ability to support childhood mental illness. Problems often have to become serious before young people can access Child & Adolescent Mental Health Services(CAMHS). CAMHS are stretched, offering help to only a quarter of those in need, and often intervene late. Early identification and treatment are beneficial, but could swamp services and create even longer waits. Some young people are reluctant to access CAMHS because of stigma (e.g. self-harm). Inequity also limits access (e.g. those experiencing economic hardship or from minority groups). These variations leave many struggling to get help, affecting their health lifelong and their and their families' lives. We need to re-think how CAMHS are delivered. Using digital tools to make CAMHS fairer and more efficient could help young people get the right treatment sooner. For example, apps or websites could be used to: (1) identify problems early before someone needs intensive treatments, (2) signpost young people to the most useful services for them rather than sending everyone to CAMHS, or (3) help predict who would benefit most from which treatments, so young people get the right treatment first time. This could be achieved by harnessing the power of 'big data'. Information (data) about a young person's life could help. For example, the risk of serious problems is indicated by an accumulation of factors such as early childhood experiences (e.g. bullying, neglect, racism), the environment (e.g. housing, diet, the amount of green space near home) or physical factors (e.g. genetics, inflammation, brain chemistry). Data like these are already collected from a range of sources such as maternity, health visitors, GP records, schools and social care, but are never brought together. This information, if brought together, could be used to create digital tools to identify patterns using artificial intelligence (AI). However, there are problems to solve first. We do not know which data are most useful, how best to bring data together securely, or the most effective AI methods. Importantly, we have not got agreement on which information should be used for which purposes. For example, it might be acceptable to use genetic information in a hospital to decide which medication is safest, but maybe not to identify who is at risk of suffering from a problem in the community. We must get this right. In this study, we will access data from a broad range of sources, some of which we will collect and organise in the early stage of this project, and use it to establish the best way to develop digital tools to support CAMHS. We will then work with the public, and experts who work with or have experience of MH problems, to translate AI algorithms into digital tools. These digital tools must be part of a clinical service that can intervene early. We want to create a new early identification and prevention service and establish what digital tools are needed to make early detection work effectively, safely, and fairly. We will bring together experts who are doing ground-breaking work in academia, industry, and the clinic, with policy makers. We want to turn their attention to solving these problems, together with young people, their carers, and people with lived experience. The people whose data is used should direct the building of these tools and new clinical pathways. We need their help thinking about which data should be used for what purposes, for which people, what should happen when a young person is thought to be developing MH problems, and how to use digital tools to support treatment decisions. In later years we will explore the effectiveness of the early identification and prevention approach, create recommendations for overhauling inefficient systems and develop a template for data-guided, individualised, and timely MH interventions for the future.