Much of the activities that universities undertake and the equipment they own can be used to benefit wider parts of society. To support innovation and learning being spread, universities undertake activities known as knowledge exchange which means interacting with and training businesses, undertaking community projects or working with charities or residents in their local area. As such, they have a function to support wider society in addition to training students and doing research. Every year the UK government gives money to each university to support this type of activity. Consequently, each university has to produce evidence about these types of things they do to show how it is performing, which determines how much each university is allocated. Because this type of work is quite new for universities compared to delivering teaching, there is no standardised way of capturing the information, nor hardly any systems or software to do it. Many have tried to adapt accountancy software or spreadsheets. Staff recording impact using pen and paper isn't unusual. This means that collecting all documentation from different sources is a time-consuming administrative task, and information can get lost if a staff leaves or their computer malfunctions. What we are proposing to build is an online system to automatically capture information, so it's all stored in one place and can be displayed for project managers to see and monitor. Furthermore, it can be used for more complicated statistical analysis. The information will never get lost, and we can do more useful things with it when it is standardised. Our project intends to do this together with university staff involved in knowledge exchange activities, using workshops to find out what information they would like to collect and then design surveys and systems to do it for them, as well as identify any barriers or problems in their jobs that prevents from making these returns to the government. This will allow universities a way to capture what they are doing, as well as help them to win more money from the central government each year. Naturally, then, the more funding each institution receives, the better the chances of creating jobs, sharing skills and transferring knowledge from the universities to their local communities become.

The use of technology in everyday life is increasing year on year. One of the methods that is becoming increasingly common to see is the use of Artificial Intelligence (AI) to support or deliver services. Frequently it is used to respond to queries when a human staff member isn't free. Or to process very large amounts of information in an efficient way. However, as it becomes more common additional requirements are needed for products to ensure they are safe for the public and also patients (if used in a healthcare setting). For example, it is known that AI software can have biases built into it without the knowledge of the teams that create them, depending on what information sources are used in their development. This project is designed to support a process known as Artificial Intelligence Assurance. That seeks to ensure AI products produce trustworthy information that is safe, appropriate and reliable while also being in compliance with relevant standards. We propose to build a set of survey systems that can support the auditing, accreditation, certification and impact assessment of AI products. Specifically we will focus on the early stage ventures and spinouts that are produced in Universities. This is because in the UK (and Europe) many companies with a technology component to them (AI in this case) will enrol on a university business accelerator or incubator programme at some point in their commercial journey. This provides a suitable space to be able to test if a company's AI software is safe, reliable and accurate. In addition, the company will have access to budgets and support from the incubator/accelerator programme to fix any issues that may arise during that time. We will build the AI assurance tools (e.g. an audit) into online surveys. This is because it allows us to automate the analysis for each of the assessments and create visualisations of the score i.e. bar-charts, pie-charts etc. Consequently, as soon as the company fills in the paperwork we know what level they are at. This rapidness will be useful to the managers of university incubator/accelerator programmes to keep track of what is happening across the institution, and make any changes that they need. It will also allow them to start to make a university wide plans (roadmap) on what to do with AI-based companies as they can observe trends in dashboard data.