The Introduction to AI assurance provides a grounding in AI assurance for readers who are unfamiliar with the subject area. This guide introduces key AI assurance concepts and terms and situates them within the wider AI governance landscape. As an introductory guide, this document focuses on the underlying concepts of AI assurance rather than technical detail, however it will include suggestions for further reading for those interested in learning more.
As AI becomes increasingly prevalent across all sectors of the economy, it’s essential that we ensure it is well governed. AI governance refers to a range of mechanisms including laws, regulations, policies, institutions, and norms that can all be used to outline processes for making decisions about AI. The goal of these governance measures is to maximize and reap the benefits of AI technologies while mitigating potential risks and harms.
In March 2023, the government published its AI governance framework in a pro-innovation approach to AI regulation. This white paper set out a proportionate, principles-based approach to AI governance, with the framework underpinned by five cross-sectoral principles. These principles describe “what” outcomes AI systems must achieve, regardless of the sector in which they’re deployed. The white paper also sets out a series of tools that can be used to help organizations understand “how” to achieve these outcomes in practice: tools for trustworthy AI, including assurance mechanisms and global technical standards.
This guidance aims to provide an accessible introduction to both assurance mechanisms and global technical standards, to help industry and regulators better understand how to build and deploy responsible AI systems. It will be a living, breathing document that we keep updated over time.
Why is AI assurance important?
Artificial intelligence (AI) offers transformative opportunities for the economy and society. The dramatic development of AI capabilities over recent years, particularly generative AI – including Large Language Models (LLMs) such as ChatGPT – has fuelled significant excitement around the potential applications for, and benefits of, AI systems.
Artificial intelligence has been used to support personalized cancer treatments, mitigate the worst effects of climate change and make transport more efficient. The potential economic benefits from AI are also extremely high. Recent research from McKinsey suggests that generative AI alone could add up to $4.4 trillion to the global economy.
However, there are also concerns about the risks and societal impacts associated with AI. There has been notable debate about the potential existential risks to humanity but there are also significant, and more immediate, concerns relating to risks such as bias, a loss of privacy and socio-economic impacts such as job losses.
When ensuring the effective deployment of AI systems many organizations recognize that, to unlock the potential of AI systems, they will need to secure public trust and acceptance. This will require a multidisciplinary and socio-technical approach to ensure that human values and ethical considerations are built-in throughout the AI development lifecycle.
AI assurance is consequently a crucial component of wider organizational risk management frameworks for developing, procuring, and deploying AI systems, as well as demonstrating compliance with existing – and any relevant future – regulation. With developments in the regulatory landscape, significant advances in AI capabilities and increased public awareness of AI, it is more important than ever for organizations to start engaging with AI assurance.
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