The UK Government’s Approach to AI Regulation
The UK government’s recently published approach to AI regulation sets out a proportionate and adaptable framework that manages risk and enhances trust while also allowing innovation to flourish.
Promoting Dialogue on Potential Paths to an Accountable AI Assurance Profession
Certification holds promise as one of a wider set of tools for trustworthy AI. In building an accountable AI assurance profession, assurance providers (both the organisations as a whole and the individual professionals themselves) could be certified to evidence their expertise and therefore their trustworthiness.
Lessons Learned from Other Certification Models
Early in 2023, we spoke to experts across a broad range of sectors to understand what works and doesn’t work in other certification models. We sought views to reflect the varied subject matter and unique challenges of different domains, including cybersecurity, aerospace, sustainability, nuclear safety, bioethics, and medical devices.
Main Takeaways
• Context is key: drivers like regulation and market forces, and governance elements like assurance standards and techniques, will influence the role of certification and how it matures over time.
• Broad community building is crucial for reliable, accountable certification.
• In a changing environment, balance between flexibility and robustness is essential.
• Therefore, existing effective certification schemes are adaptable, managing this balance appropriately. They are also transparent and interoperable.
• To be effective, certification schemes require a broad range of stakeholder views.
• Continual monitoring and evaluation can manage complexity.
Lesson One: Context is Key
Certification is one of many governance tools, so it is important to consider it within its broader context. The wider governance landscape, including principles, standards, and conformity assessment techniques, must develop before certification can be effective. Across the range of sectors and schemes we considered, certification was consistently one of the final governance elements to mature.
Governance Context for Certification
Certification is one of many governance tools, so it is important to consider it within its broader context. The wider governance landscape, including principles, standards, and conformity assessment techniques, must develop before certification can be effective. The development and adoption of certification may be driven by a combination of factors, including regulation and market forces.
Questions About Certification
There are some important questions about what role voluntary certification schemes should play in both the long and short term. Certification evaluates whether something meets a certain standard. However, many standards for AI are still being developed and agreed upon. As such, for the time being "soft" voluntary certification schemes may not be sufficiently developed for establishing and communicating trust.
Conclusion
The broader context for certification will continue to emerge and develop further over time. However, in the immediate term, we should consider and seek consensus on whether encouraging voluntary certification now can help create and mature effective schemes that can be used in the future—taking an iterative approach to certification, aligned with the UK government’s adaptable approach to AI regulation.
FAQs
Q: What is the role of certification in AI assurance?
A: Certification holds promise as one of a wider set of tools for trustworthy AI.
Q: Why is context important in AI certification?
A: Certification is one of many governance tools, so it is important to consider it within its broader context.
Q: How do market forces influence certification in AI?
A: Market forces can encourage certification, as differentiation and brand recognition create competitive advantages and incentives for voluntary certification to demonstrate compliance with good practice, norms or standards, positively affecting consumers’ trust.
Q: Can voluntary certification schemes be effective in AI?
A: Voluntary certification schemes can be effective, but only if they are developed and adopted in a way that takes into account the unique challenges of AI and the need for alignment with emerging assurance techniques and technical standards.
