AI Assurance Overview provides the basics of AI Assurance for readers who are not familiar with the subject matter. This guide introduces key AI assurance concepts and terminology and situates them within the broader AI governance environment. As an introductory guide, this document focuses on the concepts underlying AI assurance rather than technical details, but includes suggestions for further reading for those who want to learn more.
As AI becomes increasingly pervasive in all sectors of the economy, it is essential to ensure that it is properly managed. AI governance refers to the various mechanisms that can be used to outline decision-making processes regarding AI, including laws, regulations, policies, institutions, and norms. The goal of these governance measures is to maximize and enjoy the benefits of AI technology while mitigating potential risks and harms.
In March 2023, the government announced the AI Governance Framework, a pro-innovation approach to AI regulation. This whitepaper sets out a proportionate, principles-based approach to AI governance, with a framework underpinned by five cross-cutting principles. These principles describe “what” an AI system should accomplish, regardless of the sector in which it is deployed. The white paper presents a set of tools to help organizations understand 'how' they achieve these results in practice: Tools for Trustworthy AI, including assurance mechanisms and global technical standards. I will also explain.
This guidance provides an accessible introduction to both assurance mechanisms and global technical standards, and aims to help industry and regulators better understand how to build and deploy responsible AI systems. That's what I mean. This will be a living document and we will continue to update it over time.
Why is AI Assurance important?
Artificial intelligence (AI) offers transformative opportunities for economies and societies. The dramatic advances in AI capabilities in recent years, particularly generative AI, including large-scale language models (LLMs) such as ChatGPT, have led to great excitement about the potential applications of AI systems and their benefits.
Artificial intelligence is being used to support personalized cancer treatments, reduce the worst effects of climate change, and make transportation more efficient. The potential economic benefits of AI are also significant. A recent study 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 social impact associated with AI. While there has been notable discussion about potential existential risks to humanity, there are also significant and more pressing concerns about risks such as stigma, loss of privacy, and socio-economic impacts such as unemployment.
When it comes to ensuring the effective deployment of AI systems, many organizations recognize that to unlock the potential of AI systems, they must ensure public trust and acceptance. This will require an interdisciplinary and socio-technical approach to ensure that human values and ethical considerations are incorporated throughout the AI development lifecycle.
AI assurance is therefore a key element of a broader organizational risk management framework for demonstrating compliance with the development, procurement, and deployment of AI systems and existing and relevant future regulations. The evolving regulatory landscape, significant advancements in AI capabilities, and growing public awareness of AI make it more important than ever for organizations to start working on AI assurance.
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