Guidelines and Resources for AI Model Access from TrusTEd Researchenvironments (GRAIMatter)

可信研究环境(TRES)为研究人员提供了一个安全的场所来分析公共利益项目的数据，例如向SAGE提供信息以抗击新冠肺炎大流行。TRE工作人员检查输出以防止个人机密数据泄露。TRE历来只支持经典的统计数据分析。越来越有必要促进人工智能(AI)模型的培训。人工智能有许多有价值的应用，例如，发现人为错误，简化流程，帮助重复任务和支持临床决策。然后，需要将经过训练的模型从tres导出以供使用。人工智能模型的规模和复杂性对披露核查过程构成了重大挑战。模型可能容易受到外部黑客的影响：反向工程学习过程以找出用于训练的数据的复杂方法，与传统统计方法相比，更有可能导致重新识别。在公众代表的输入下，GRAIMatter将评估一系列工具和方法，以支持从人工智能方法的输出中评估潜在可识别的信息，调查法律和伦理影响和控制，并产生一套指南和建议，以支持所有具有人工智能算法出口控制的TRE。

Trusted Research Environments (TREs) provide a secure location for researchers to analyse data for projects in the public interest e.g. providing information to SAGE to fight the COVID-19 pandemic. TRE staff check outputs to prevent disclosure of individuals’ confidential data.TREs have historically supported only classical statistical data analysis. There is an increasing need to also facilitate the training of Artificial Intelligence (AI) models. AI has many valuable applications e.g., spotting human errors, streamlining processes, helping with repetitive tasks and supporting clinical decision making. The trained models then need to be exported from TREs for use. The size and complexity of AI models presents significant challenges for the disclosure-checking process. Models may be susceptible to external hacking: complicated methods to reverse engineer the learning process to find out about the data used for training, with more potential to lead to re-identification than conventional statistical methods.With input from public representatives, GRAIMatter will assess a range of tools and methods to support TREs to assess output from AI methods for potentially identifiable information, investigate the legal and ethical implications and controls, and produce a set of guidelines and recommendations to support all TREs with export controls of AI algorithms.