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.

Overview: We propose a Centre for Doctoral Training in Data Science. Data science is an emerging discipline that combines machine learning, databases, and other research areas in order to generate new knowledge from complex data. Interest in data science is exploding in industry and the public sector, both in the UK and internationally. Students from the Centre will be well prepared to work on tough problems involving large-scale unstructured and semistructured data, which are increasingly arising across a wide variety of application areas. Skills need: There is a significant industrial need for students who are well trained in data science. Skilled data scientists are in high demand. A report by McKinsey Global Institute cites a shortage of up to 190,000 qualified data scientists in the US; the situation in the UK is likely to be similar. A 2012 report in the Harvard Business Review concludes: "Indeed the shortage of data scientists is becoming a serious constraint in some sectors." A report on the Nature web site cited an astonishing 15,000% increase in job postings for data scientists in a single year, from 2011 to 2012. Many of our industrial partners (see letters of support) have expressed a pressing need to hire in data science. Training approach: We will train students using a rigorous and innovative four-year programme that is designed not only to train students in performing cutting-edge research but also to foster interdisciplinary interactions between students and to build students' practical expertise by interacting with a wide consortium of partners. The first year of the programme combines taught coursework and a sequence of small research projects. Taught coursework will include courses in machine learning, databases, and other research areas. Years 2-4 of the programme will consist primarily of an intensive PhD-level research project. The programme will provide students with breadth throughout the interdisciplinary scope of data science, depth in a specialist area, training in leadership and communication skills, and appreciation for practical issues in applied data science. All students will receive individual supervision from at least two members of Centre staff. The training programme will be especially characterized by opportunities for combining theory and practice, and for student-led and peer-to-peer learning.

This Centre for Doctoral training in Industrially Focused Mathematical Modelling will train the next generation of applied mathematicians to fill critical roles in industry and academia. Complex industrial problems can often be addressed, understood, and mitigated by applying modern quantitative methods. To effectively and efficiently apply these techniques requires talented mathematicians with well-practised problem-solving skills. They need to have a very strong grasp of the mathematical approaches that might need to be brought to bear, have a breadth of understanding of how to convert complex practical problems into relevant abstract mathematical forms, have knowledge and skills to solve the resulting mathematical problems efficiently and accurately, and have a wide experience of how to communicate and interact in a multidisciplinary environment. This CDT has been designed by academics in close collaboration with industrialists from many different sectors. Our 35 current CDT industrial partners cover the sectors of: consumer products (Sharp), defence (Selex, Thales), communications (BT, Vodafone), energy (Amec, BP, Camlin, Culham, DuPont, GE Energy, Infineum, Schlumberger x2, VerdErg), filtration (Pall Corp), finance (HSBC, Lloyds TSB), food and beverage (Nestle, Mondelez), healthcare (e-therapeutics, Lein Applied Diagnostics, Oxford Instruments, Siemens, Solitonik), manufacturing (Elkem, Saint Gobain), retail (dunnhumby), and software (Amazon, cd-adapco, IBM, NAG, NVIDIA), along with two consultancy companies (PA Consulting, Tessella) and we are in active discussion with other companies to grow our partner base. Our partners have five key roles: (i) they help guide and steer the centre by participating in an Industrial Engagement Committee, (ii) they deliver a substantial elements of the training and provide a broad exposure for the cohorts, (iii) they provide current challenges for our students to tackle for their doctoral research, iv) they give a very wide experience and perspective of possible applications and sectors thereby making the students highly flexible and extremely attractive to employers, and v) they provide significant funding for the CDT activities. Each cohort will learn how to apply appropriate mathematical techniques to a wide range of industrial problems in a highly interactive environment. In year one, the students will be trained in mathematical skills spanning continuum and discrete modelling, and scientific computing, closely integrated with practical applications and problem solving. The experience of addressing industrial problems and understanding their context will be further enhanced by periods where our partners will deliver a broad range of relevant material. Students will undertake two industrially focused mini-projects, one from an academic perspective and the other immersed in a partner organisation. Each student will then embark on their doctoral research project which will allow them to hone their skills and techniques while tackling a practical industrial challenge. The resulting doctoral students will be highly sought after; by industry for their flexible and quantitative abilities that will help them gain a competitive edge, and by universities to allow cutting-edge mathematical research to be motivated by practical problems and be readily exploitable.

Observed, Strategic, sustained action is now needed to avoid further negative consequences of climate change and to build a greener, cleaner and fairer future. According to the Intergovernmental Panel on Climate Change the rise in global temperature is largely driven by total carbon dioxide emissions over time. In order to avoid further global warming, international Governments agreed to work towards a balance between emissions and greenhouse gas removal (GGR), known 'net zero', in the Paris Agreement. In June 2019 the UK committed to reaching net zero emissions by 2050, making it the first G7 country to legislate such a target. Transitioning to net zero means that we will have to remove as many emissions as we produce. Much of the focus of climate action to date has been on reducing emissions, for example through renewable power and electric vehicles. However, pathways to net zero require not just cutting fossil fuel emissions but also turning the land into a net carbon sink and scaling up new technologies to remove and store greenhouse gases. This will require new legislation to pave the way for investment in new infrastructure and businesses expected to be worth billions of pounds a year within 30 years. This challenge has far-reaching implications for technology, business models, social practices and policy. GGR has been much less studied, developed and incentivised than actions to cut emissions. The proposed CO2RE Hub brings together leading UK academics with a wide range of expertise to co-ordinate a suite of GGR demonstration projects to accelerate progress in this area. In particular the Hub will study how we can (1) reduce technology costs so that GGR becomes economically viable; (2) ensure industry adopts the concept of net zero in a way that will maintain and create jobs; (3) put in place sensible policy incentives; (4) make sure there is social license for GGR (unlike fracking or nuclear); (5) set up regulatory oversight of environmental sustainability and risks of GGR; (6) understand what is required to achieve GGR at large scale and (7) guarantee there are the skills and knowledge required for all this to happen. Building on extensive existing links to stakeholders in business, Government and NGOs, the Hub will work extensively with everyone involved in regulating and delivering GGR to ensure our research provides solutions to strategic priorities. We will also encourage the teams working on demonstrator technologies to think responsibly about the risks, benefits and public perceptions of their work and consider the full environmental, social and economic implications of implementation from the outset. CO2RE will seek to bring the GGR community in the UK as a whole closer together, functioning as a gateway to UK inter-disciplinary research expertise on GGR. We will inform, and stay informed, about the latest developments nationally and internationally, and reach out to engage the wider public. In doing so we will be able to respond to a rapidly evolving landscape recognising that technical and social change are not separate, but happen together. To accelerate and achieve meaningful change, we will be guided by consultation with key decision-makers and the general public, and set up a £1m flexible fund to respond to priorities that emerge with the help of the wider UK academic community. Ultimately we will help the UK and the world understand how GGR can be scaled up responsibly as part of climate action to meet the ambition of net zero.

Technological advances have done, and will do, much to improve cybersecurity. But, a technological approach is only part of the solution - achieving digital security is inherently a socio-technical endeavour. By combining world-leading research with challenge fellows from across the social sciences, expert working groups, innovative approaches to networking and agile, industry-facing commissioning, the DiScriBe Hub+ will not only address the challenges faced by the ISCF Digital Security by Design (DSbD) initiative, but will fundamentally reshape the ways in which social sciences and STEM disciplines work together to address the challenges of digital security by design in the 21st Century. The core missions of the DiScriBe Hub+ are to provide interdisciplinary leadership to realise digital security by design by connecting social science to a hardware layer that rarely receives support or engagement from social science. This social science input will help to unleash the transformational potential that the hardware innovations within Digital Security by Design makes possible. The Hub+ has five main ways of doing this: 1) Running a series of deep engagements with DSbD stakeholders using techniques from the arts and humanities in order to elicit a shared view of 'Digital Security by Design Futures' 2) Conducting an innovative programme of interdisciplinary research to improve our understanding of the barriers and incentives around adoption of new secure architectures, business readiness levels and adoption, regulatory opportunities and challenges, and ways these are experienced and understood across diverse sectors; 3) Commissioning a range of agile, responsive, industry-facing projects and 'connecting capabilities' grants to address specific DSbD challenges; 4) Establishing a network of 'challenge fellows' tasked with synthesising research outcomes (core and commissioned), connecting insights to the wider Digital Security by Design initiative, and ensuring impact, alongside expert working groups comprising industry and researchers to tackle specific problems in a sharp, focussed way; and, 5) Building a community of social scientists, hardware engineers, software developers, industry and policy makers who are deeply engaged in applying a socio-technical lens to digital security by design. DiScriBe is unique in its focus on the benefits of connecting security architecture innovation with leading social science - and will provide a step change in how cybersecurity is treated as an inter-disciplinary, social as well as technical, problem. Many of the lessons on cross disciplinary working will be tested and embedded through close working with the Bristol Digital Futures Institute - a £70m investment in how our ways of working will need to change in the digital future. We have expert challenge fellows who are leading social scientists applying their work to cybersecurity for the first time. These fellows will also lead working groups on specific topics connecting industry, policy and academia, which in turn will lead to a range of open calls for commissioned industry-facing research. This research will be both theoretically rigorous within social science, while also remaining responsive and agile enough to meet the needs of the wider DSbD programme. As a consequence, a major outcome of DiScriBE will not only be a vibrant, new community, but novel insights that can be applied to the development and implementation of new security-related developments.