Predictive modelling for HEI-Commercialisation Dashboard

科技在日常生活中的使用逐年增加。越来越常见的方法之一是使用人工智能(AI)来支持或提供服务。当人类工作人员没有空闲时，它经常被用来响应查询。或者以高效的方式处理非常大量的信息。然而，随着它变得越来越普遍，需要对产品提出额外的要求，以确保它们对公众和患者都是安全的(如果在医疗保健环境中使用)。例如，众所周知，人工智能软件可能在不知道创建它们的团队的情况下具有内置的偏见，这取决于他们的开发中使用了什么信息源。该项目旨在支持一个名为人工智能保障的过程。这旨在确保人工智能产品产生安全、适当和可靠的可信信息，同时也符合相关标准。我们建议建立一套调查系统，支持对人工智能产品的审计、认证、认证和影响评估。具体而言，我们将重点关注大学产生的早期企业和衍生产品。这是因为，在英国(和欧洲)，许多拥有技术元素的公司(这里指的是人工智能)将在其商业旅程的某个时候注册参加大学商业加速器或孵化器项目。这为能够测试一家公司的AI软件是否安全、可靠和准确提供了合适的空间。此外，该公司将获得孵化器/加速器计划的预算和支持，以解决在此期间可能出现的任何问题。我们将在在线调查中构建人工智能保证工具(例如审计)。这是因为它允许我们自动化每个评估的分析，并创建分数的可视化，即条形图、饼图等。因此，一旦公司填写了文书工作，我们就知道它们处于哪个级别。这一快速将有助于大学孵化器/加速器项目的管理者跟踪整个机构正在发生的事情，并做出他们需要的任何改变。它还将允许他们开始制定大学范围的计划(路线图)，说明如何处理基于人工智能的公司，因为他们可以观察仪表盘数据的趋势。

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.