Generative AI Framework for HM Government is an extensive document that outlines a framework for the use of generative AI in the UK Government. The framework is structured around ten guiding principles, which encompass understanding generative AI and its limitations, using it lawfully and ethically, ensuring security, maintaining human control, managing its lifecycle, choosing the right tools, collaborating, working with commercial colleagues, having the necessary skills, and aligning with organizational policies.
The document discusses the nature of generative AI, its applications in government, and its limitations, particularly with large language models (LLMs). It emphasizes the importance of building generative AI solutions that are aligned with specific goals and considering use cases that leverage the technology effectively while avoiding inappropriate uses.
A significant focus is placed on using generative AI safely and responsibly, addressing legal considerations, ethical issues, fairness, bias, discrimination, information quality, misinformation, human involvement in automated processes, sustainability, and environmental considerations. The document also delves into aspects of data protection and privacy.
Security is another critical area covered, including deploying generative AI securely, understanding the associated risks, and implementing effective governance. This includes the formation of AI governance boards or committees, creating AI/ML system inventories, and considering program governance in teams. The document guides defining goals for using generative AI, such as improving public services, productivity, staff satisfaction, quality, cost savings, and risk reduction. It also advises on identifying suitable use cases and avoiding inappropriate ones, stressing the importance of understanding user needs and solving the right problems.
Consumer-side generative AI applications, such as LLMs offered by various providers, are mentioned. These services, often free, allow users to interact with AI models, but users are cautioned to align with organizational policies and understand the terms of service, especially concerning data use and potential biases in AI outputs.
Core concepts in designing and building generative AI solutions are outlined, focusing on prompts as primary inputs to LLMs, prompt engineering for improved performance and accuracy, and the use of meta-prompts for directing AI responses.
Finally, the document discusses embedded generative AI applications integrated into existing products. These tools use language-based prompts for various tasks and are noted for their straightforward user interfaces. Examples include tools for image editing, code development, AI assistance in cloud environments, and support for using Microsoft and Google products. Users are advised to understand the scope of data access and processing with these tools, especially concerning data sovereignty and organizational data access.
You can download the full document here
