Algorithms increasingly govern interactions between state and citizen and as the 'digital by default' model of government-citizen interaction spreads this will increase. This increase, combined with the value of data science and how AI and machine learning is embraced as a way to achieve efficiency and carry out public policy we need to consider how algorithms mediate real-world relationships between the state and individuals. Without confidence in the legitimacy and credibility of the algorithms the trust between government and citizens will dramatically degrade. Our research will therefore focus on algorithmic interactions between the citizen and the state and examine how we form productive and trusted relationships between those designing, deploying and using the algorithmic interactions and the communities affected by the decisions. We will examine three key public policy areas where algorithmic decision-making is used for aspects of policy deployment: refugee resettlement, welfare and healthcare provision. These three areas have been selected as they are at the forefront of services that developed as part of digital by default, where issues of cost are addressed, in part, by algorithmic decision making to evaluate legitimate service access and use. Additionally, these are areas of significant public spending where the intended users of these services are more likely feel excluded and disenfranchised from mainstream society. Our research will examine how the re-designing of the system interactions and the communication of the political and economic logic will enhance the security and well-being of individuals, protects the security of the state and increases the confidence in digital service design.

We are living in an unprecedented age where vast quantities of our personal data are continually recorded and analysed, for example, our travel patterns, shopping habits and fitness routines. Our daily lives are now tied into this evolving loop of data collection, leading to data-based automated decisions, that can make recommendations and optimise our routines. There is tremendous economic and societal value in understanding this deluge of unstructured disparate data streams. A key challenge in Artificial Intelligence (AI) research is to extract meaningful value from these data sources to make decisions that can be trusted and understood to improve society. The PASCAL research programme is focused on developing an end-to-end framework, from data to decisions, that naturally accounts for data uncertainty and provides transparent and interpretable decision-making tools. The algorithms developed throughout this research project will be generally-applicable in a wide range of application domains and appropriate for modern computer hardware infrastructure. All of the research and associated algorithms will be widely available through high-quality open-source software that will ensure the widest possible uptake of this research within the international AI research community. PASCAL will focus on two primary applications areas: cybersecurity and transportation, which will stimulate and motivate this research and ensure wide-spread impact within these sectors. To drive through the impact and uptake of this research within these sectors, we will work closely with committed strategic partners, GCHQ, the Heilbronn Institute of Mathematical Research, Transport Research Laboratory, the University of Washington and the Alan Turing Institute. Cybersecurity - The proliferation of computers and mobile technology over the last few decades has led to an exponential increase in recorded data. Much of this data is personally, economically and nationally sensitive and protecting it is a key priority for any government or large organisation. Threats to data security exist on a global scale and identifying potential threats requires cybersecurity experts to evaluate and extract critical intelligence from complex and evolving data sources. In order to model and understand the intricate patterns between these data sources requires complex mathematical models. The PASCAL programme will develop new algorithms that maintain the richness of these mathematical models and use them to provide interpretable and transparent decision recommendations. Autonomous vehicles (AV) - The transition to AVs will be the most significant global change in transportation for the past century. The economic benefit and successful implementation of this technology within the UK requires a thorough understanding of the risks posed by driverless vehicles and what new procedures are required to ensure human safety. Through PASCAL, we will develop a framework to artificially-generate realistic traffic scenarios to test AVs under a wide range of road conditions and create criteria to safely accredit AV vehicles in the UK.

Since the early 2000s public service in the UK has undergone significant re-design and a fundamental part of the vision is to produce services used everyday by people that are safe and secure for all. Acknowledging the importance of safe and secure public services, this fellowship is specifically grounded in that area of service design and focuses on the connections between the ways that people create feelings of safety and security in their everyday lives and the protection of digital everyday services. In the design of digital services, responses to concerns related to trust, identity, privacy and security have typically been handled as part of the digital interaction between service user and service provider and yet the techniques that people use to protect personal privacy, keep information confidential, build trust and manage identity are also enmeshed in their everyday routines and practices. Whilst human factors considerations are a long-established part of this security design process, the focus is typically more on designing for user interaction and the protection of their data rather than designing more broadly for the safety and security of people in their everyday lives. As everyday services are increasingly digitised and reach into almost every aspect of a person's life, it becomes a priority to link these two aspects of protection so that everyday practices become a part of a service engagement that protects an individual's privacy, trust and identity as well as contributing to their individual security. Security in the context of everyday life is much wider than protection from technological attack; security is also the freedom to engage with these new forms of public service free from concern about threats to their personal safety, security or privacy. In this context not only must technological attack be considered but so too must service providers such as housing authorities, local councils and health care professionals being regarded as threat actors and malicious acts against individuals by family and friends through the misuse of public services be considered. When traditional service providers and members of a person's kin and friendship networks are regarded as sources of threat, people will deploy a wide range of social as well as technological practices to defend themselves. Successfully designing to support and improve these defences through social practices are as important as the design of technological defences. Outputs This fellowship will develop a framework through which researchers can co-research and co-design with communities, develop interventions and create impactful techniques that support and improve social defences. Through the research framework relationships will be built between researchers, service producer and consumer communities and practitioners from the areas of everyday security and technological security design. The fellowship programme will produce a handbook of real-world security-focused everyday service design research problems to be used as part of education programmes as well as the researcher communities. Additionally on-line engagements will be run periodically throughout the fellowship to promote broader thinking about designing to support trust, identity, privacy and security in everyday services. This fellowship programme will also produce innovative technologies. Examples of possible prototypes include: sound and tactile maps to convey the lived experience of particular communities of service consumers, mapping techniques to show networks of trust across a geographical area, skills-swap technologies to facilitate knowledge transfer about trust, identity, privacy and security in a digitally mediated society and the development of virtual reality technology to help develop understanding of what identity, trust, security and privacy conflicts mean to different communities.

We live in a society dominated by information. The collection of data is an ongoing and continuous process, covering all aspects of life, and the amount of data available in recent years has exploded. In order to make sense of this data, utilise it, gain insights and draw conclusions, new computational methods to analyse and infer have been developed. This is often described by the general term "artificial intelligence" (AI), which includes "machine learning" or "deep learning", which rely on the processing of information by computers to extract nontrivial information, without providing explicit models. Highly visible are developments driven by social media, as this affects every person in a very explicit manner. However, AI is widely adopted across the industrial sectors and hence underpins a successful growth of the UK's economy. Moreover, also in academic research AI has become a toolset used across the disciplines, beyond the traditional realms of computer and data science. Research in science, health and engineering relies on AI to support a wide range of activities, from the discovery of the Higgs boson and gravitational waves via the detection of breast cancer and diabetic retinopathy to autonomous decision- making and human-machine interaction. In order to sustain the industrial growth, it is necessary to train the next generation of highly-skilled AI users and researchers. In this Centre for Doctoral Training, we deliver a training programme for doctoral researchers covering a broad range of scientific and medical topics, and with external partners engaged at every level, from large international companies via government agencies to SMEs and start-ups. AI relies on computing and with data sets growing ever larger, the use of advanced computing skills, such as optimisation, parallelisation and scalability, becomes a necessity for the bigger tasks. For that reason the CDT has joined forces with Supercomputing Wales (SCW), a new £15 million national supercomputing programme of investment, part-funded by the European Regional Development Fund. The CDT will connect researchers working at Swansea, Aberystwyth, Bangor, Cardiff and Bristol universities with regional and national industrial partners and with SCW. Our CDT is therefore ideally placed to link AI and high-performance computing in a coordinated fashion. The academic foundation of our training programme is built on research excellence. We focus on three broad multi- disciplinary scientific, medical and computational areas, namely - data from large science facilities, such as the Large Hadron Collider, the Square Kilometre Array and the Laser Interferometer Gravitational-Wave Observatory; - biological, health and clinical sciences, including access to electronic health records, maintained in the Secured Anonymised Information Linkage databank; - novel mathematical, physical and computer science approaches, driving future developments in e.g. visualisation, collective intelligence and quantum machine learning. Our researchers will therefore be part of cutting-edge global science activities, be able to modernise public health and determine the future landscape of AI. We recognise that AI is a multidisciplinary activity, which extends far beyond single disciplines or institutions. Training and engagement will hence take place across the universities and industrial partners, which will stimulate interaction. Ideally, a doctoral researcher should be able to apply their skills on a research topic in, say, health informatics, particle physics or deep learning, and be able to contribute equally. To ensure our training is aligned with the demands from industry, the CDT's industrial partners will co-create the training programme, provide input in research problems and highlight industrial challenges. As a result our researchers will grow into flexible and creative individuals, who will be fluent in AI skills and well-placed for both industry and academia.

The equipment requested will provide new capability and internationally leading facilities that will enable cutting-edge research and internationally leading science, beyond that which is possible with current instrumentation. The equipment will also facilitate greater collaborative opportunities with other Universities and industry nationally and internationally. The "Advanced Electronic Materials and Devices" bundle provides equipment for research into new materials and devices for future electronic applications, ranging from superconductors for applications in power transmission and MRI to spintronic devices for sensors and computer memory applications. It will also improve thermal imaging capability for the study of semiconductor and hybrid diamond based devices which have the potential to transform future power electronic devices. Electrical power conversion technologies have a vital role to play in managing energy demand and improving energy conversion efficiency, affording 'game-changes' in, for example, low carbon transport systems and energy supply networks. As these 'more electric' systems become more commonplace, for example through their adoption in aircraft and electric vehicles, new understanding of operation life and failure modes is needed. The enhanced capabilities offered by the equipment updates in the "Enabling robust design and analysis of electrical power conversion systems" will allow internationally leading research to be pursued in the areas of design for life, virtual certification and reliability. Transmission electron microscopes (TEM) allow the imaging of both the external and internal structure of materials and are available in numerous configurations dependent on the specific nature of the materials under investigation. A post column energy filter dramatically improves the analytical and imaging capabilities of a TEM by allowing structural and chemical information carried by the electrons to be interrogated after interaction with the sample material. The requested Gatan Imaging Filter (GIF) upgrade in the "Supporting Analysis of Advanced Energy Materials and Soft Matter" will provide significant new capability to determine the structure and composition of materials at the nanoscale and provide new insights into how to enhance material functionality. The instrument upgrade forms part of a strategic investment in advanced microscopy provision at Bristol, and reflects ambitions for an internationally competitive materials characterization facility befitting the world-leading research it underpins. Nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) are the pre-eminent techniques for studying chemical structure and reactions. They underpin nearly every program of research in catalysis (accelerating chemical reactions), synthesis (creation of new chemical entities) and materials (chemicals with defined properties and applications e.g. nanotechnology). The replacement of aging NMR and MS instruments as described in the "Underpinning Catalysis, Synthesis and Materials Chemistry" bundle will ensure continued cutting-edge investigations in these fields, and will provide new hardware capabilities that allow the study of molecular/chemical systems in previously impossible fashions, e.g., at low temperature for days at a time (NMR), or under unreactive atmospheres (MS). The new "Wideband Multi-channel Real-time Wireless Channel Emulator" facility will offer wideband (160MHz) multi-dimensional channel (8 x 8) wireless channel emulation for sub 6GHz wireless transceivers allowing repeatable experimentation with real-world channel models (3GPP and 802.11, plus user defined scenarios). The hardware can also be reconfigured to offer channel emulation with cascaded bandwidths synonymous with millimetre wave operation, thus driving forward the "5G and beyond" research agenda.