Towards an Equitable Social VR Social Virtual Reality (SVR) constructs a digital parallel to the physical world, enabling remote social engagement mediated by modern immersive Virtual Reality (VR) technology. This social engagement is not strictly limited to conventional social interaction, but has also recently expanded to include activities such as remote participation in training, work, and service delivery. This digital parallel world offers significant opportunities for greater inclusion of individuals who are currently marginalised by the physical world, thereby widening access to the Digital Economy. SVR is a rapidly emerging technology and its pace of adoption has accelerated in the global pandemic. However, to date, there has been limited research examining the accessibility and inclusion requirements of SVR for users who currently face digital access barriers due to a disability or age-related capability loss. As a society, we sit at a critical juncture where concepts of inclusion and accessibility can be embedded into SVR while the technology is still in its formative stage. Towards an Equitable Social VR addresses the need to ensure that SVR platforms are accessible and inclusive for people with disabilities and older people, thus allowing for the potential of the platforms in contributing to the quality of life of these population groups to be realised in full. The project will undertake a programme of R&D with the aim of delivering the SVR Inclusion Framework: a collection of formalised guidance and tools serving to facilitate equal participation in SVR for disabled and older users. The project will take into account the whole spectrum of capability loss manifestations, including vision, hearing, mobility, dexterity, and neurodiversity aspects of cognition (learning difficulties) and mental health, as well as the co-occurrence of capability loss.

With the prevalence of data-hungry deep learning approaches in Artificial Intelligent (AI) as the de facto standard, now more than ever there is a need for labelled data. However, while there have been interesting recent discussions on the definition of readiness levels of data, the same type of scrutiny on annotations is still missing in general: we do not know how or when the annotations were collected or what their inherent biases are. Additionally, there are now forms of annotation beyond standard static sets of labels that call for a formalisation and redefinition of the annotation concept (e.g., rewards in reinforcement learning or directed links in causality). During this Fellowship we will design and establish the protocols for transparent annotations that empowers the data curator to report on the process, the practitioner to automatically evaluate the value of annotations and the users to provide the most informative and actionable feedback. This Fellowship will address all these through a holistic human-centric research agenda, bridging gaps in fundamental research and public engagement with AI. The Fellowship aims to lay the foundations for a two-way approach to annotations, where the paradigm is shifted from annotations simply being a resource to them becoming a means for AI systems and humans to interact. The bigger picture is that, with annotations seen as an interface between both entities, we will be in a much better position to guide the relation of trust in between learning systems and users, where users translate their preferences into the learning systems' objective functions. This approach will help produce a much needed transformation in how potentially sensitive aspects of AI become a step closer to being reliable and trustworthy.

Optimisation -- the problem of identifying a satisficing solution among a vast set of candidates -- is not only a fundamental problem in Artificial Intelligence and Computer Science, but essential to the competitiveness of UK businesses. Real-world optimisation problems are often tackled using evolutionary algorithms, which are optimisation techniques inspired by Darwin's principles of natural selection. Optimisation with classical evolutionary algorithms has a fundamental problem. These algorithms depend on a user-provided fitness function to rank candidate solutions. However, for real world problems, the quality of candidate solutions often depend on complex adversarial effects such as competitors which are difficult for the user to foresee, and thus rarely reflected in the fitness function. Solutions obtained by an evolutionary algorithm using an idealised fitness function, will therefore not necessarily perform well when deployed in a complex and adversarial real-world setting. So-called co-evolutionary algorithms can potentially solve this problem. They simulate a competition between two populations, the "prey" which attempt to discover good solutions, and the "predators" which attempt to find flaws in these. This idea greatly circumvents the need for the user to provide a fitness function which foresees all ways solutions can fail. However, due to limited understanding of their working principles, co-evolutionary algorithms are plagued by a number of pathological behaviours, including loss of gradient, relative over-generalisation, and mediocre objective stasis. The causes and potential remedies for these pathological behaviours are poorly understood, currently limiting the usefulness of these algorithms. The project has been designed to bring a break-through in the theoretical understanding of co-evolutionary algorithms. We will develop the first mathematically rigorous theory which can predict when a co-evolutionary algorithm reaches a solution efficiently, and when pathological behaviour occurs. This theory has the potential to make co-evolutionary algorithms a reliable optimisation method for complex real-world problems.

The future information infrastructure will be characterized by massive streaming sets of distributed data-sources. These data will challenge classical statistical and machine learning methodologies both from a computational and a theoretical perspective. This proposal investigates a flexible class of models for learning and inference in the context of these challenges. We will develop learning infrastructures that are powerful, flexible and 'privacy aware' with a user-centric focus. These learning infrastructures will be developed in the context of particular application challenges, including mental health, the developing world and personal information management. These applications are inspired by collaborations with citizenme, the NewMind Network for Mental Health Technology Research and Makerere University in Kampala, Uganda.

1) To create the next generation of Natural Language Processing experts, stimulating the growth of NLP in the public and private sectors domestically and internationally. A pool of NLP talent will provide incentives for (existing) companies to expand their operations in the UK and lead to start-ups and new products. 2) To deliver a programme which will have a transformative effect on the students that we train and on the field as a whole, developing future leaders and producing cutting-edge research in both methodology and applications. 3) To give students a firm grounding in the challenge of working with language in a computational setting and its relevance to critical engineering and scientific problems in our modern world. The Centre will also train them in the key programming, engineering, and machine learning skills necessary to solve NLP problems. 4) To attract students from a broad range of backgrounds, including computer science, AI, maths and statistics, linguistics, cognitive science, and psychology and provide an interdisciplinary cohort training approach. The latter involves taught courses, hands-on laboratory projects, research-skills training, and cohort-based activities such as specialist seminars, workshops, and meetups. 5) To train students with awareness of user design, ethics and responsible research in order to design systems that improve user statisfaction, treat users fairly, and increase the uptake of NLP technology across cultures, social groups and languages.