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AI regulation: A pro-innovation approach – EU vs UK

In this article, the writers compare the United Kingdom’s plans for implementing a pro-innovation approach to regulation (“UK Approach”) versus the European Union’s proposed Artificial Intelligence Act (the “EU AI Act”).

Authors: Sean Musch, AI & Partners and Michael Borrelli, AI & Partners

AI – The opportunity and the challenge

AI currently delivers broad societal benefits, from medical advances to mitigating climate change. As an example, an AI technology developed by DeepMind, a UK- based business, can predict the structure of almost every protein known to science. Government frameworks consider the role of regulation in creating the environment for AI to flourish. AI technologies have not yet reached their full potential. Under the right conditions, AI will transform all areas of life and stimulate economies by unleashing innovation and driving productivity, creating new jobs and improving the workplace.

The UK has indicated a requirement to act quickly to continue to lead the international conversation on AI governance and demonstrate the value of our pragmatic, proportionate regulatory approach. In their report, the UK government identify the short time frame for intervention to provide a clear, pro-innovation regulatory environment in order to make the UK one of the top places in the world to build foundational AI companies. Not too dissimilar to this EU legislators have signalled an intention to make the EU a global hub for AI innovation. On both fronts responding to risk and building public trust are important drivers for regulation. Yet, clear and consistent regulation can also support business investment and build confidence in innovation.

What remains critical for the industry is winning and retaining consumer trust, which is key to the success of innovation economies. Neither the EU nor the UK can afford not to have clear, proportionate approaches to regulation that enable the responsible application of  AI to flourish. Without such consideration, they risk creating cumbersome rules applying to all AI technologies.

What are the policy objectives and intended effects?

Similarities exist in terms of the overall aims. As shown in the table below, the core similarities revolve around growth, safety and economic prosperity:

What are the problems being tackled?

Again, similarities exist in terms of a common focus: the end-user. AI’s involvement in multiple activities of the economy, whether this be from simple chatbots to biometric identification, inevitably mean that end-users end up being affected. Protecting them at all costs seems to be the presiding theme:

What are the differences in policy options?

A variety of options have been considered by the respective policymakers. On the face of it, pro-innovation requires a holistic examination to account for the variety of challenges new ways of working generate. The EU sets the standard with Option 3:

What are the estimated direct compliance costs to firms?

Both the UK Approach and the EU AI Act regulatory framework will apply to all AI systems being designed or developed, made available or otherwise being used in the EU/UK, whether they are developed in the EU/UK or abroad. Both businesses that develop and deploy AI, “AI businesses”, and businesses that use AI, “AI adopting businesses”, are in the scope of the framework. These two types of firms have different expected costs per business under the respective frameworks.

UK Approach: Key assumptions for AI system costs

Key finding: Cost of compliance for HRS highest under Option 3

EU AI Act: Total compliance cost of the five requirements for each AI product

Key finding: Information provision represents the highest cost incurred by firms.

In light of these comparisons, it appears the EU estimates a lower cost of compliance compared to the UK. Lower costs don’t confer a less rigid approach. Rather, they indicate an itemised approach to cost estimation as well as using a standard pricing metric, hours. In practice, firms are likely to aim to make this more efficient by reducing the number of hours required to achieve compliance.

Lessons from the UK Approach for the EU AI Act

The forthcoming EU AI Act is set to place the EU at the global forefront of regulating this emerging technology. Accordingly, models for the governance and mitigation of AI risk from outside the region can still provide insightful lessons for EU decision-makers to learn and issues to account for before the EU AI Act is passed.

This is certainly applicable to Article 9 of the EU AI Act, which requires developers to establish, implement, document, and maintain risk management systems for high-risk AI systems. There are three key ideas for EU decision-makers to consider from the UK Approach.

AI assurance techniques and technical standards

Unlike Article 17 of the EU AI Act, the quality management system put in place by providers of high-risk AI systems is designed to ensure compliance. To do this, providers of high-risk  AI  systems must establish techniques, procedures, and systematic actions to be used for development, quality control, and quality assurance. The EU AI Act only briefly covers the concept of assurance, but it could benefit from publishing assurance techniques and technical standards that play a critical role in enabling the responsible adoption of AI so that potential harms at all levels of society are identified and documented.

To assure AI systems effectively, the UK government is calling for a toolbox of assurance techniques to measure, evaluate, and communicate the trustworthiness of AI systems across the development and deployment life cycle. These techniques include impact assessment, audit, and performance testing along with formal verification methods. To help innovators understand how AI assurance techniques can support wider AI governance, the government launched a ‘Portfolio of AI Assurance techniques’ in Spring 2023. This is an industry collaboration to showcase how these tools are already being applied by businesses to real-world use cases and how they align with the AI regulatory principles.

Similarly, assurance techniques need to be underpinned by available technical standards, which provide a common understanding across assurance providers. Technical standards and assurance techniques will also enable organisations to demonstrate that their systems are in line with the regulatory principles enshrined under the EU AI Act and the UK Approach. Similarities exist in terms of the stage of development.

Specifically, the EU AI Act defines common mandatory requirements applicable to the design and development of certain AI systems before they are placed on the market that will be further operationalised through harmonised technical standards. In equal fashion, the UK government intends to have a leading role in the development of international technical standards, working with industry, international and UK partners. The UK government plans to continue to support the role of technical standards in complementing our approach to AI regulation, including through the UK AI Standards Hub. These technical standards may demonstrate firms’ compliance with the EU AI Act.

A harmonised vocabulary

All relevant parties would benefit from reaching a consensus on the definitions of key terms related to the foundations of AI regulation. While the EU AI Act and the UK Approach are either under development or in the incubation stage, decision-makers for both initiatives should seize the opportunity to develop a shared understanding of core AI ideas, principles, and concepts, and codify these into a harmonised transatlantic vocabulary. As shown below, identification of where both initiatives are in agreement, and where they diverge, has been undertaken:

How AI & Partners can help

We can help you start assessing your AI systems using recognised metrics ahead of the expected changes brought about by the EU AI Act. Our leading practice is geared towards helping you identify, design, and implement appropriate metrics for your assessments.