AI Regulation

UK AIAI Regulation

In the UK, our AI Compliance Advisors are significantly influencing the evolving landscape of AI regulation. Their expertise is central to the development of national AI regulatory frameworks, aligning with the UK’s unique legal and technological context. This involvement is crucial as the UK navigates its post-Brexit regulatory environment, seeking to establish itself as a leader in AI innovation while ensuring responsible and ethical AI use.

Overview of Topic

At present, there is no comprehensive legal framework in the UK that oversees the creation, implementation, or usage of Artificial Intelligence (AI).  In 2023 alone, the UK has released a consultation on a policy document titled ‘A pro-innovation approach to AI regulation’ (by the Department for Science, Innovation and Technology and the Office for Artificial Intelligence), initiated the formation of a £100 million Foundation Model Taskforce, and proclaimed that the UK will be the venue for a global summit on AI Safety. While the EU takes a rules-based approach to AI governance, the UK is suggesting a ‘contextual, sector-based regulatory framework’, anchored in institutions and this diffuse network of existing regulatory regimes. 

Significance in Today's Landscape

The Bank of England (including the Prudential Regulation Authority (PRA)), and the Financial Conduct Authority (FCA) published a discussion paper (DP) 5/22 – Artificial Intelligence and Machine Learning – in October 2022 to further their understanding and to deepen dialogue on how AI may affect their respective objectives for the prudential and conduct supervision of financial firms. The DP is part of the supervisory authorities’ wider programme of work related to AI, including the AI Public Private Forum (AIPPF), and its final report published in February 2022, the FS2/23 – Artificial Intelligence and Machine Learning.

A summary of the key points includes:

•        A regulatory definition of AI is deemed unnecessary by many respondents, who favor principles-based or risk-based approaches focusing on AI’s characteristics and risks.

•        Given the rapid evolution of AI, regulators should consider creating ‘live’ regulatory guidance that is periodically updated with best practices.

•        Ongoing industry engagement, like the AIPPF, is vital and could serve as a model for continuous public-private collaboration.

•        The regulatory landscape regarding AI is seen as complex and fragmented. Greater coordination among domestic and international regulators is needed.

•        There is a consensus on the fragmentation of data regulation. More regulatory alignment is needed, particularly in areas concerning fairness, bias, and the management of protected characteristics.

•        Regulation and supervision should prioritize consumer outcomes, especially in ensuring fairness and ethical aspects.

•        The increasing use of third-party models and data raises concerns, necessitating more regulatory guidance. The relevance of DP3/22 to the UK financial sector is noted.

•        AI systems’ complexity across various firm areas suggests that a unified approach, especially between data management and model risk management, is beneficial.

•        While the principles in CP6/22 (now SS1/23) are seen as sufficient for AI model risk, some areas need strengthening or clarification.

•        Existing firm governance structures, like the SM&CR, are considered adequate for addressing AI risks.


UK firms that relies on AI models in their decision making.

Asset Managers
Commodity Houses

How Can We Help?

The main factors contributing to AI-related risks in financial services are centered around three critical phases of the AI lifecycle: data, models, and governance. Risks that originate at the data stage can influence the model stage, subsequently leading to more extensive challenges at the firm’s level, particularly concerning the management of AI systems. The way AI is employed in financial services can lead to various outcomes and risks at each of these three stages (data, models, and governance), all of which are significant for the oversight roles of supervisory authorities. Nonetheless some of our client can get ahead with following up on the below:

The following steps can summarise it:


AI Ethics Consultations

T3 AI compliance advisors can offering consultations on ethical AI use and help to develop ethical guidelines for AI deployment.


Technical Auditing

Our Technical Auditors can run audits to ensure AI systems are built and operating in compliance with legal and ethical standards and identify biases and other issues in AI algorithms


Data Governance

Our financial data experts can assist in the establishment of robust data governance frameworks and ensuring data privacy and security compliance.


Documentation and Reporting

T3 can help document AI systems, processes, and data handling procedures for regulatory compliance and assist in the preparation of compliance reports and other required documentation.


Algorithm Transparency and Explainability

Our AI modellers can help enhance the transparency and explainability of AI algorithms in order to create clear, understandable explanations of how AI systems make decisions.


Impact Assessments

This involves conducting AI impact assessments to evaluate the potential risks and benefits of AI projects and identifying potential negative impacts and suggesting mitigation strategies.


Third-party Vendor Assessment

We work with numerous vendors and can help with assessing the compliance of third-party vendors and partners in the AI ecosystem and ensuring that external partners adhere to required legal and ethical standards.


Customized Compliance Solutions

T3 can develop tailored compliance frameworks and solutions based on the specific needs and risks associated with a company’s AI projects and implementing compliance monitoring and management systems.


Incident Response Planning

Our senior compliance advisors can work with your legal council to prepare companies for potential legal or ethical issues related to AI and develop incident response plans to manage and mitigate risks.

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