The UK is the third largest generator of wind power in Europe, with 584 projects, 4,366 turbines and four of the five largest European wind farms. Conflicts between wind energy generation and bats - animals with high legal protection across Europe - therefore have important implications for the economy and energy security as well as biodiversity. We are currently concluding research that has quantified the scale of collision and disturbance impacts and examined potential predictors of risk. This is the only work in the UK to address this issue at commercial scale wind energy installations. The purpose of the current project is to determine with stakeholders the practical applications of the environmental data and expertise amassed during this extensive and costly research, and to package these with the assistance of users into accessible formats to facilitate more effective management of the environmental impacts of wind energy production. Stakeholders have emphasised to us that evidence-based decision making requires that they not only have access to the overall results of scientific analyses, but to information and guidance on which to base best-practice for future commercial surveys and monitoring. Because of our extensive research, we have available a unique dataset on bat activity and casualty rates at wind turbine sites across the UK, as well as unparalleled experience in practical monitoring techniques: this project will allow these to be shared with end-users. Specific outputs will include species- and region-specific reference ranges for bat activity levels, allowing stakeholders to contextualise and interpret the bat activity levels routinely recorded in surveys conducted by ecological consultants; Geographic Information System (GIS) layers to facilitate evidence-based decision making about cumulative ecological impacts; information on appropriate monitoring techniques; and assistance with understanding the potential consequences of developments for local and national bat populations. The direct beneficiaries will be wind energy developers and operators (industry), professional ecological consultants (service providers), local government ecologists and planning committees (decision makers), and Statutory Nature Conservation Organisations (SNCOs, policy makers). Keywords: environmental impact assessment; wind turbines; bats; ecological data; wind energy Stakeholders: Statutory Nature Conservation Organisations (Natural Resources Wales, Natural England, Scottish Natural Heritage) Local Authority Ecologists and Planners (including The Association of Local Government Ecologists) Professional Ecological Consultants (including the Chartered Institute of Ecology and Environmental Management) Department for Environment, Food and Rural Affairs Department of Energy and Climate Change Wind energy developers and operators (including all of the major energy suppliers as well as installers of small energy systems) Non-governmental wildlife conservation organisations (e.g. Bat Conservation Trust, The Wildlife Trusts)

Our mission is to train the next generations of innovators in responsible, data-driven and knowledge-intensive human-in-the-loop AI systems. Our innovative, cohort-based training programme will deliver cohorts of highly trained PhD graduates with the skills to design and implement complex interactive AI pipelines solving societally important problems in responsible ways. While fully autonomous artificial intelligence dominates today's headlines in the form of self-driving cars and human-level game play, the key AI challenges of tomorrow are posed by the need for interactive knowledge-intensive systems in which the human plays an essential role, be it as an end-user providing relevant case-specific knowledge or interrogating the system, an operator requiring crucial information to be presented in an intelligible form, a supervisor requiring confirmation that the system's performance remains within acceptable limits, or a regulator assessing to what extent the system operates according to exacting standards concerning transparency, accountability and fairness. Each of these examples demonstrates a need for specific and meaningful interaction between the AI system and human(s). The examples also demonstrate the importance of knowledge for achieving human-level interaction, in addition to the data driving the machine learning aspect of the system. In close conversation with our industry partners we thus identified Interactive Artificial Intelligence (IAI) as a core sub-discipline of AI where the need for and deficit in advanced AI skills is abundantly evident while being homogeneous enough to have intellectual integrity and be taught and researched within the context of a single CDT. The most important aspects of the training programme are: - Knowledge-Driven AI and Data-Driven AI are core components treated in a close symbiotic relationship: the former uses knowledge in processes such as reasoning, argumentation and dialogue, but in such a way that data is treated as a first-class citizen; the latter starts from data but emphasises knowledge-intensive forms of machine learning such as relational learning which take knowledge as an additional input. - Human-AI Interaction is another core component addressing all human-in-the-loop aspects, overseen by a co-investigator from the human-computer interaction field. - Responsible AI is underpinning not just the taught first year but the students' doctoral training throughout all four years, overseen by two dedicated co-investigators with backgrounds in IT law and industrial codes of practice. Other skill requirements from stakeholders include: the ability to design and implement complete end-to-end systems; acquiring depth in some AI-related subjects without sacrificing breadth; the ability to work in teams of people with diverse skill sets; and being able to take on a role as "AI ambassadors" who are able to inspire but also to manage expectations through their in-depth understanding of the strengths and weaknesses of different AI techniques. The IAI training programme is designed to achieve this by strongly emphasising cohort-based training. Students will develop their projects and coursework within an innovative software environment which means easy integration of their work with that of others. This virtual hub is complemented by a physical hub where all cohorts are colocated -- together both hubs will strongly promote interaction both within and between cohorts: e.g., projects can aim at improving or extending software produced by the previous cohort, so that senior students can be involved in mentoring their juniors. In summary, the IAI training programme pulls together Bristol's unique and comprehensive strengths in doctoral training and AI to deliver highly trained AI innovators, equipping them with essential skills to deliver the interactive AI technology society requires to deal with current and future challenges.