Regulating artificial intelligence (AI) has become the challenge of the time. This is a crucial area of regulatory development and there are increasing calls—including from those driving the development of AI—for robust regulatory and governance systems. In this context, more details have now emerged on the UK’s approach to AI regulation.
Swimming against the tide, and seeking to diverge from the EU’s regulatory agenda and the EU AI Act, the UK announced a light-touch ‘pro-innovation approach’ in its July 2022 AI regulation policy paper. In March 2023, the same approach was supported by a Report of the Government Chief Scientific Adviser (the ‘GCSA Report’), and is now further developed in the White Paper ‘AI regulation: a pro-innovation approach’ (the ‘AI WP’). The UK Government has launched a public consultation that will run until 21 June 2023.
Given the relevance of the issue, it can be expected that the public consultation will attract a large volume of submissions, and that the ‘pro-innovation approach’ will be heavily criticised. Indeed, there is an on-going preparatory Parliamentary Inquiry on the Governance of AI that has already collected a wealth of evidence exploring the pros and cons of the regulatory approach outlined there. Moreover, initial reactions eg by the Public Law Project, the Ada Lovelace Institute, or the Royal Statistical Society have been (to different degrees) critical of the lack of regulatory ambition in the AI WP—while, as could be expected, think tanks closely linked to the development of the policy, such as the Alan Turing Institute, have expressed more positive views.
Whether the regulatory approach will shift as a result of the expected pushback is unclear. However, given that the AI WP follows the same deregulatory approach first suggested in 2018 and is strongly politically/policy entrenched—for the UK Government has self-assessed this approach as ‘world leading’ and claims it will ‘turbocharge economic growth’—it is doubtful that much will necessarily change as a result of the public consultation.
That does not mean we should not engage with the public consultation, but the opposite. In the face of the UK Government’s dereliction of duty, or lack of ideas, it is more important than ever that there is a robust pushback against the deregulatory approach being pursued. Especially in the context of public sector digitalisation and the adoption of AI by the public administration and in the provision of public services, where the Government (unsurprisingly) is unwilling to create regulatory safeguards to protect citizens from its own action.
In this blogpost, I sketch my main areas of concern with the ‘pro-innovation approach’ in the GCSA Report and AI WP, which I will further develop for submission to the public consultation, building on earlier views. Feedback and comments would be gratefully received: a.sanchez-graells@bristol.ac.uk.
The ‘pro-innovation approach’ in the GCSA Report — squaring the circle?
In addition to proposals on the intellectual property (IP) regulation of generative AI, the opening up of public sector data, transport-related, or cyber security interventions, the GCSA Report focuses on ‘core’ regulatory and governance issues. The report stresses that regulatory fragmentation is one of the key challenges, as is the difficulty for the public sector in ‘attracting and retaining individuals with relevant skills and talent in a competitive environment with the private sector, especially those with expertise in AI, data analytics, and responsible data governance‘ (at 5). The report also further hints at the need to boost public sector digital capabilities by stressing that ‘the government and regulators should rapidly build capability and know-how to enable them to positively shape regulatory frameworks at the right time‘ (at 13).
Although the rationale is not very clearly stated, to bridge regulatory fragmentation and facilitate the pooling of digital capabilities from across existing regulators, the report makes a central proposal to create a multi-regulator AI sandbox (at 6-8). The report suggests that it could be convened by the Digital Regulatory Cooperation Forum (DRCF)—which brings together four key regulators (the Information Commissioner’s Office (ICO), Office of Communications (Ofcom), the Competition and Markets Authority (CMA) and the Financial Conduct Authority (FCA))—and that DRCF should look at ways of ‘bringing in other relevant regulators to encourage join up’ (at 7).
The report recommends that the AI sandbox should operate on the basis of a ‘commitment from the participant regulators to make joined-up decisions on regulations or licences at the end of each sandbox process and a clear feedback loop to inform the design or reform of regulatory frameworks based on the insights gathered. Regulators should also collaborate with standards bodies to consider where standards could act as an alternative or underpin outcome-focused regulation’ (at 7).
Therefore, the AI sandbox would not only be multi-regulator, but also encompass (in some way) standard-setting bodies (presumably UK ones only, though), without issues of public-private interaction in decision-making implying the exercise of regulatory public powers, or issues around regulatory capture and risks of commercial determination, being considered at all. The report in general is extremely industry-orientated, eg in stressing in relation to the overarching pacing problem that ‘for emerging digital technologies, the industry view is clear: there is a greater risk from regulating too early’ (at 5), without this being in any way balanced with clear (non-industry) views that the biggest risk is actually in regulating too late and that we are collectively frog-boiling into a ‘runaway AI’ fiasco.
Moreover, confusingly, despite the fact that the sandbox would be hosted by DRCF (of which the ICO is a leading member), the GCSA Report indicates that the AI sandbox ‘could link closely with the ICO sandbox on personal data applications’ (at 8). The fact that the report is itself unclear as to whether eg AI applications with data protection implications should be subjected to one or two sandboxes, or the extent to which the general AI sandbox would need to be integrated with sectoral sandboxes for non-AI regulatory experimentation, already indicates the complexity and dubious practical viability of the suggested approach.
It is also unclear why multiple sector regulators should be involved in any given iteration of a single AI sandbox where there may be no projects within their regulatory remit and expertise. The alternative approach of having an open or rolling AI sandbox mechanism led by a single AI authority, which would then draw expertise and work in collaboration with the relevant sector regulator as appropriate on a per-project basis, seems preferable. While some DRCF members could be expected to have to participate in a majority of sandbox projects (eg CMA and ICO), others would probably have a much less constant presence (eg Ofcom, or certainly the FCA).
Remarkably, despite this recognition of the functional need for a centralised regulatory approach and a single point of contact (primarily for industry’s convenience), the GCSA Report implicitly supports the 2022 AI regulation policy paper’s approach to not creating an overarching cross-sectoral AI regulator. The GCSA Report tries to create a ‘non-institutionalised centralised regulatory function’, nested under DRCF. In practice, however, implementing the recommendation for a single AI sandbox would create the need for the further development of the governance structures of the DRCF (especially if it was to grow by including many other sectoral regulators), or whichever institution ‘hosted it’, or else risk creating a non-institutional AI regulator with the related difficulties in ensuring accountability. This would add a layer of deregulation to the deregulatory effect that the sandbox itself creates (see eg Ranchordas (2021)).
The GCSA Report seems to try to square the circle of regulatory fragmentation by relying on cooperation as a centralising regulatory device, but it does this solely for the industry’s benefit and convenience, without paying any consideration to the future effectiveness of the regulatory framework. This is hard to understand, given the report’s identification of conflicting regulatory constraints, or in its terminology ‘incentives’: ‘The rewards for regulators to take risks and authorise new and innovative products and applications are not clear-cut, and regulators report that they can struggle to trade off the different objectives covered by their mandates. This can include delivery against safety, competition objectives, or consumer and environmental protection, and can lead to regulator behaviour and decisions that prioritise further minimising risk over supporting innovation and investment. There needs to be an appropriate balance between the assessment of risk and benefit’ (at 5).
This not only frames risk-minimisation as a negative regulatory outcome (and further feeds into the narrative that precautionary regulatory approaches are somehow not legitimate because they run against industry goals—which deserves strong pushback, see eg Kaminski (2022)), but also shows a main gap in the report’s proposal for the single AI sandbox. If each regulator has conflicting constraints, what evidence (if any) is there that collaborative decision-making will reduce, rather than exacerbate, such regulatory clashes? Are decisions meant to be arrived at by majority voting or in any other way expected to deactivate (some or most) regulatory requirements in view of (perceived) gains in relation to other regulatory goals? Why has there been no consideration of eg the problems encountered by concurrency mechanisms in the application of sectoral and competition rules (see eg Dunne (2014), (2020) and (2021)), as an obvious and immediate precedent of the same type of regulatory coordination problems?
The GCSA report also seems to assume that collaboration through the AI sandbox would be resource neutral for participating regulators, whereas it seems reasonable to presume that this additional layer of regulation (even if not institutionalised) would require further resources. And, in any case, there does not seem to be much consideration as to the viability of asking of resource-strapped regulators to create an AI sandbox where they can (easily) be out-skilled and over-powered by industry participants.
In my view, the GCSA Report already points at significant weaknesses in the resistance to creating any new authorities, despite the obvious functional need for centralised regulation, which is one of the main weaknesses, or the single biggest weakness, in the AI WP—as well as in relation to a lack of strategic planning around public sector digital capabilities, despite well-recognised challenges (see eg Committee of Public Accounts (2021)).
The ‘pro-innovation approach’ in the AI WP — a regulatory blackhole, privatisation of ai regulation, or both
The AI WP envisages an ‘innovative approach to AI regulation [that] uses a principles-based framework for regulators to interpret and apply to AI within their remits’ (para 36). It expects the framework to ‘pro-innovation, proportionate, trustworthy, adaptable, clear and collaborative’ (para 37). As will become clear, however, such ‘innovative approach’ solely amounts to the formulation of high-level, broad, open-textured and incommensurable principles to inform a soft law push to the development of regulatory practices aligned with such principles in a highly fragmented and incomplete regulatory landscape.
The regulatory framework would be built on four planks (para 38): [i] an AI definition (paras 39-42); [ii] a context-specific approach (ie a ‘used-based’ approach, rather than a ‘technology-led’ approach, see paras 45-47); [iii] a set of cross-sectoral principles to guide regulator responses to AI risks and opportunities (paras 48-54); and [iv] new central functions to support regulators to deliver the AI regulatory framework (paras 70-73). In reality, though, there will be only two ‘pillars’ of the regulatory framework and they do not involve any new institutions or rules. The AI WP vision thus largely seems to be that AI can be regulated in the UK in a world-leading manner without doing anything much at all.
AI Definition
The UK’s definition of AI will trigger substantive discussions, especially as it seeks to build it around ‘the two characteristics that generate the need for a bespoke regulatory response’: ‘adaptivity’ and ‘autonomy’ (para 39). Discussing the definitional issue is beyond the scope of this post but, on the specific identification of the ‘autonomy’ of AI, it is worth highlighting that this is an arguably flawed regulatory approach to AI (see Soh (2023)).
No new institutions
The AI WP makes clear that the UK Government has no plans to create any new AI regulator, either with a cross-sectoral (eg general AI authority) or sectoral remit (eg an ‘AI in the public sector authority’, as I advocate for). The Ministerial Foreword to the AI WP already stresses that ‘[t]o ensure our regulatory framework is effective, we will leverage the expertise of our world class regulators. They understand the risks in their sectors and are best placed to take a proportionate approach to regulating AI’ (at p2). The AI WP further stresses that ‘[c]reating a new AI-specific, cross-sector regulator would introduce complexity and confusion, undermining and likely conflicting with the work of our existing expert regulators’ (para 47). This however seems to presume that a new cross-sector AI regulator would be unable to coordinate with existing regulators, despite the institutional architecture of the regulatory framework foreseen in the AI WP entirely relying on inter-regulator collaboration (!).
No new rules
There will also not be new legislation underpinning regulatory activity, although the Government claims that the WP AI, ‘alongside empowering regulators to take a lead, [is] also setting expectations‘ (at p3). The AI WP claims to develop a regulatory framework underpinned by five principles to guide and inform the responsible development and use of AI in all sectors of the economy: [i] Safety, security and robustness; [ii] Appropriate transparency and explainability; [iii] Fairness; [iv] Accountability and governance; and [v] Contestability and redress (para 10). However, they will not be put on a statutory footing (initially); ‘the principles will be issued on a non-statutory basis and implemented by existing regulators’ (para 11). While there is some detail on the intended meaning of these principles (see para 52 and Annex A), the principles necessarily lack precision and, worse, there is a conflation of the principles with other (existing) regulatory requirements.
For example, it is surprising that the AI WP describes fairness as implying that ‘AI systems should (sic) not undermine the legal rights of individuals or organisations, discriminate unfairly against individuals or create unfair market outcomes‘ (emphasis added), and stresses the expectation ‘that regulators’ interpretations of fairness will include consideration of compliance with relevant law and regulation’ (para 52). This encapsulates the risks that principles-based AI regulation ends up eroding compliance with and enforcement of current statutory obligations. A principle of AI fairness cannot modify or exclude existing legal obligations, and it should not risk doing so either.
Moreover, the AI WP suggests that, even if the principles are supported by a statutory duty for regulators to have regard to them, ‘while the duty to have due regard would require regulators to demonstrate that they had taken account of the principles, it may be the case that not every regulator will need to introduce measures to implement every principle’ (para 58). This conflates two issues. On the one hand, the need for activity subjected to regulatory supervision to comply with all principles and, on the other, the need for a regulator to take corrective action in relation to any of the principles. It should be clear that regulators have a duty to ensure that all principles are complied with in their regulatory remit, which does not seem to entirely or clearly follow from the weaker duty to have due regard to the principles.
perpetuating regulatory gaps, in particular regarding public sector digitalisation
As a consequence of the lack of creation of new regulators and the absence of new legislation, it is unclear whether the ‘regulatory strategy’ in the AI WP will have any real world effects within existing regulatory frameworks, especially as the most ambitious intervention is to create ‘a statutory duty on regulators requiring them to have due regard to the principles’ (para 12)—but the Government may decide not to introduce it if ‘monitoring of the effectiveness of the initial, non-statutory framework suggests that a statutory duty is unnecessary‘ (para 59).
However, what is already clear that there is no new AI regulation in the horizon despite the fact that the AI WP recognises that ‘some AI risks arise across, or in the gaps between, existing regulatory remits‘ (para 27), that ‘there may be AI-related risks that do not clearly fall within the remits of the UK’s existing regulators’ (para 64), and the obvious and worrying existence of high risks to fundamental rights and values (para 4 and paras 22-25). The AI WP is naïve, to say the least, in setting out that ‘[w]here prioritised risks fall within a gap in the legal landscape, regulators will need to collaborate with government to identify potential actions. This may include identifying iterations to the framework such as changes to regulators’ remits, updates to the Regulators’ Code, or additional legislative intervention’ (para 65).
Hoping that such risk identification and gap analysis will take place without assigning specific responsibility for it—and seeking to exempt the Government from such responsibility—seems a bit too much to ask. In fact, this is at odds with the graphic depiction of how the AI WP expects the system to operate. As noted in (1) in the graph below, it is clear that the identification of risks that are cross-cutting or new (unregulated) risks that warrant intervention is assigned to a ‘central risk function’ (more below), not the regulators. Importantly, the AI WP indicates that such central function ‘will be provided from within government’ (para 15 and below). Which then raises two questions: (a) who will have the responsibility to proactively screen for such risks, if anyone, and (b) how has the Government not already taken action to close the gaps it recognises exists in the current legal landscape?
AI WP Figure 2: Central risks function activities.
This perpetuates the current regulatory gaps, in particular in sectors without a regulator or with regulators with very narrow mandates—such as the public sector and, to a large extent, public services. Importantly, this approach does not create any prohibition of impermissible AI uses, nor sets any (workable) set of minimum requirements for the deployment of AI in high-risk uses, specially in the public sector. The contrast with the EU AI Act could not be starker and, in this aspect in particular, UK citizens should be very worried that the UK Government is not committing to any safeguards in the way technology can be used in eg determining access to public services, or by the law enforcement and judicial system. More generally, it is very worrying that the AI WP does not foresee any safeguards in relation to the quickly accelerating digitalisation of the public sector.
Loose central coordination leading to ai regulation privatisation
Remarkably, and in a similar functional disconnect as that of the GCSA Report (above), the decision not to create any new regulator/s (para 15) is taken in the same breath as the AI WP recognises that the small coordination layer within the regulatory architecture proposed in the 2022 AI regulation policy paper (ie, largely, the approach underpinning the DRCF) has been heavily criticised (para 13). The AI WP recognises that ‘the DRCF was not created to support the delivery of all the functions we have identified or the implementation of our proposed regulatory framework for AI’ (para 74).
The AI WP also stresses how ‘[w]hile some regulators already work together to ensure regulatory coherence for AI through formal networks like the AI and digital regulations service in the health sector and the Digital Regulation Cooperation Forum (DRCF), other regulators have limited capacity and access to AI expertise. This creates the risk of inconsistent enforcement across regulators. There is also a risk that some regulators could begin to dominate and interpret the scope of their remit or role more broadly than may have been intended in order to fill perceived gaps in a way that increases incoherence and uncertainty’ (para 29), which points at a strong functional need for a centralised approach to AI regulation.
To try and mitigate those regulatory risks and shortcomings, the AI WP proposes the creation of ‘a number of central support functions’, such as [i} a central monitoring function of overall regulatory framework’s effectiveness and the implementation of the principles; [ii] central risk monitoring and assessment; [iii] horizon scanning; [iv] supporting testbeds and sandboxes; [v] advocacy, education and awareness-raising initiatives; or [vi] promoting interoperability with international regulatory frameworks (para 14, see also para 73). Cryptically, the AI WP indicates that ‘central support functions will initially be provided from within government but will leverage existing activities and expertise from across the broader economy’ (para 15). Quite how this can be effectively done outwith a clearly defined, adequately resourced and durable institutional framework is anybody’s guess. In fact, the AI WP recognises that this approach ‘needs to evolve’ and that Government needs to understand how ‘existing regulatory forums could be expanded to include the full range of regulators‘, what ‘additional expertise government may need’, and the ‘most effective way to convene input from across industry and consumers to ensure a broad range of opinions‘ (para 77).
While the creation of a regulator seems a rather obvious answer to all these questions, the AI WP has rejected it in unequivocal terms. Is the AI WP a U-turn waiting to happen? Is the mention that ‘[a]s we enter a new phase we will review the role of the AI Council and consider how best to engage expertise to support the implementation of the regulatory framework’ (para 78) a placeholder for an imminent project to rejig the AI Council and turn it into an AI regulator? What is the place and role of the Office for AI and the Centre for Data Ethics and Innovation in all this?
Moreover, the AI WP indicates that the ‘proposed framework is aligned with, and supplemented by, a variety of tools for trustworthy AI, such as assurance techniques, voluntary guidance and technical standards. Government will promote the use of such tools’ (para 16). Relatedly, the AI WP relies on those mechanisms to avoid addressing issues of accountability across AI life cycle, indicating that ‘[t]ools for trustworthy AI like assurance techniques and technical standards can support supply chain risk management. These tools can also drive the uptake and adoption of AI by building justified trust in these systems, giving users confidence that key AI-related risks have been identified, addressed and mitigated across the supply chain’ (para 84). Those tools are discussed in much more detail in part 4 of the AI WP (paras 106 ff). Annex A also creates a backdoor for technical standards to directly become the operationalisation of the general principles on which the regulatory framework is based, by explicitly identifying standards regulators may want to consider ‘to clarify regulatory guidance and support the implementation of risk treatment measures’.
This approach to the offloading of tricky regulatory issues to the emergence of private-sector led standards is simply an exercise in the transfer of regulatory power to those setting such standards, guidance and assurance techniques and, ultimately, a privatisation of AI regulation.
A different approach to sandboxes and testbeds?
The Government will take forward the GCSA recommendation to establish a regulatory sandbox for AI, which ‘will bring together regulators to support innovators directly and help them get their products to market. The sandbox will also enable us to understand how regulation interacts with new technologies and refine this interaction where necessary’ (p2). This thus is bound to hardwire some of the issues mentioned above in relation to the GCSA proposal, as well as being reflective of the general pro-industry approach of the AI WP, which is obvious in the framing that the regulators are expected to ‘support innovators directly and help them get their products to market’. Industrial policy seems to be shoehorned and mainstreamed across all areas of regulatory activity, at least in relation to AI (but it can then easily bleed into non-AI-related regulatory activities).
While the AI WP indicates the commitment to implement the AI sandbox recommended in the GCSA Report, it is by no means clear that the implementation will be in the way proposed in the report (ie a multi-regulator sandbox nested under DRCF, with an expectation that it would develop a crucial coordination and regulatory centralisation effect). The AI WP indicates that the Government still has to explore ‘what service focus would be most useful to industry’ in relation to AI sandboxes (para 96), but it sets out the intention to ‘focus an initial pilot on a single sector, multiple regulator sandbox’ (para 97), which diverges from the approach in the GCSA Report, which would be that of a sandbox for ‘multiple sectors, multiple regulators’. While the public consultation intends to gather feedback on which industry sector is the most appropriate, I would bet that the financial services sector will be chosen and that the ‘regulatory innovation’ will simply result in some closer cooperation between the ICO and FCA.
Regulator capabilities — ai regulation on a shoestring?
The AI WP turns to the issue of regulator capabilities and stresses that ‘While our approach does not currently involve or anticipate extending any regulator’s remit, regulating AI uses effectively will require many of our regulators to acquire new skills and expertise’ (para 102), and that the Government has ‘identified potential capability gaps among many, but not all, regulators’ (para 103).
To try to (start to) address this fundamental issue in the context of a devolved and decentralised regulatory framework, the AI WP indicates that the Government will explore, for example, whether it is ‘appropriate to establish a common pool of expertise that could establish best practice for supporting innovation through regulatory approaches and make it easier for regulators to work with each other on common issues. An alternative approach would be to explore and facilitate collaborative initiatives between regulators – including, where appropriate, further supporting existing initiatives such as the DRCF – to share skills and expertise’ (para 105).
While the creation of ‘common regulatory capacity’ has been advocated by the Alan Turing Institute, and while this (or inter-regulator secondments, for example) could be a short term fix, it seems that this tries to address the obvious challenge of adequately resourcing regulatory bodies without a medium and long-term strategy to build up the digital capability of the public sector, and to perpetuate the current approach to AI regulation on a shoestring. The governance and organisational implications arising from the creation of common pool of expertise need careful consideration, in particular as some of the likely dysfunctionalities are only marginally smaller than current over-reliance on external consultants, or the ‘salami-slicing’ approach to regulatory and policy interventions that seems to bleed from the ’agile’ management of technological projects into the realm of regulatory activity, which however requires institutional memory and the embedding of knowledge and expertise.
