AI regulation gaps
Need for an AI regulatory framework that effectively addresses the identified challenges of AI governance.
Source spread
Where this theme appears
This theme appears across 7 independent accountability sources, so the source mix matters as much as the headline total.
1 PFD report
422 committee recs
2 HSSIB recs
1 HMICFRS rec
8 NAO recs
1 Scottish FAI
Browse by source
Source-grouped records are useful for tracing where a concern came from. Large sections show the 50 strongest matches for that source; counts still show the full theme total.
Select committee recommendations(422)— showing 50 strongest matches
#18 —
Recommendation: We believe that the current regulatory regime is broadly capable of addressing the issues identified during this inquiry, notwithstanding the emerging risks arising from consumers’ growing use of unregulated AI tools. We do not consider that further prescriptive rules are …
Report: Government response awaited
#17 —
Recommendation: We do not think it is clear whether AI models currently provide consumers with financial advice that would otherwise be regulated if provided by a person or firm. While some witnesses have suggested that the regulatory perimeter needs to be …
Report: Government response awaited
#16 —
Recommendation: The growing use of general-purpose AI tools by consumers to research home and travel insurance presents an emerging risk to consumers. They may provide inaccurate or unreliable information and introduce data protection concerns. The FCA considers that these tools currently …
Report: Government response awaited
#5 —
Recommendation: Consumers are increasingly using AI to research insurance products. While this has the potential to benefit consumers and improve their engagement and understanding of products, the use of AI carries a risk of harm and may provide misleading or incorrect …
Report: Government response awaited
#38 —
Recommendation: The Government should convene a programme of discussions with the UK’s AUKUS and NATO allies aimed at reaching a mutual understanding of each other’s AI objectives and strategies. These discussions should also explore reaching, where possible, common positions and standards …
Report: Government response published
#17 —
Recommendation: Research cooperation also ensures a deep mutual understanding of our approaches to emerging technologies at an upstream stage, which helps to maintain an overall compatibility of approaches including regulation. (Conclusion, Paragraph 74)
Report: No government response linked; deadline passed
#29 —
Recommendation: The challenge of providing accurate, impartial and trusted news to audiences is growing as more people access news through unregulated platforms. It is a particular challenge when it comes to the younger audiences. This trend is exacerbated by the fact …
Report: Government response awaited
#61 —
Recommendation: The IPO code should include a mechanism for rightsholders to check training data. This would provide assurance about the level of compliance with copyright law.
Report: Government response published
#58 —
Recommendation: If the process remains unresolved by Spring 2024 the Government must set out options and prepare to resolve the dispute definitively, including legislative changes if necessary.
Report: Government response published
#56 —
Recommendation: In response to this report the Government should publish its view on whether copyright law provides sufficient protections to rightsholders, given recent advances in LLMs. If this identifies major uncertainty the Government should set out options for updating legislation to …
Report: Government response published
#55 —
Recommendation: The application of the law to LLM processes is complex, but the principles remain clear. The point of copyright is to reward creators for their efforts, prevent others from using works without permission, and incentivise innovation. The current legal framework …
Report: Government response published
#54 —
Recommendation: LLMs may offer immense value to society. But that does not warrant the violation of copyright law or its underpinning principles. We do not believe it is fair for tech firms to use rightsholder data for commercial purposes without permission …
Report: Government response published
#52 —
Recommendation: We urge the Government and regulators to work with partners at pace on developing accredited standards and auditing practices for LLMs (noting that these must not be tick-box exercises). A consistent approach to publishing key information on model cards would …
Report: Government response published
#51 —
Recommendation: Accredited standards and auditing practices are key. They would help catalyse a domestic AI assurance industry, support business clarity and empower regulators.
Report: Government response published
#49 —
Recommendation: The Government should develop mandatory safety tests for high-risk high-impact models. This must include an expectation that the results will be shared with the Government (and regulators if appropriate), and clearly defined powers to require compliance with safety recommendations, suspend …
Report: Government response published
#48 —
Recommendation: We welcome the commitments from model developers to engage with the Government on safety. But it would be naïve to believe voluntary agreements will suffice in the long-term as increasingly powerful models proliferate across the world, including in states which …
Report: Government response published
#47 —
Recommendation: The Government should ask the Law Commission to review legal liability across the LLM value chain, including open access models. The Government should provide an initial position , and a timeline for establishing further legal clarity, in its White Paper …
Report: Government response published
#46 —
Recommendation: Model developers bear some responsibility for the products they are building—particularly given the foreseeable risk of harm from misuse and the limited information available to customers about how the base model works. But how far such liability extends remains unclear.
Report: Government response published
#45 —
Recommendation: The Government’s central support functions should work with regulators at pace to publish cross-sector guidance on AI issues that fall outside individual sector remits.
Report: Government response published
#44 —
Recommendation: The Government should introduce standardised powers for the main regulators who are expected to lead on AI oversight to ensure they can gather information relating to AI processes and conduct technical, empirical and governance audits. It should also ensure there …
Report: Government response published
#43 —
Recommendation: Relying on existing regulators to ensure good outcomes from AI will only work if they are properly resourced and empowered.
Report: Government response published
#42 —
Recommendation: DSIT should prioritise resourcing the teams responsible for regulatory support and co-ordination , and publish an update on staffing and policy progress in response to this report.
Report: Government response published
#41 —
Recommendation: We support the overall White Paper approach. But the pace of delivering the central support functions is inadequate. The regulatory support and co-ordination teams proposed in the March 2023 White Paper underpin its entire success. By the end of November …
Report: Government response published
#40 —
Recommendation: Setting the strategic direction for LLMs and developing enforceable, pro-innovation regulatory frameworks at pace should remain the Government’s immediate priority.
Report: Government response published
#39 —
Recommendation: Extensive primary legislation aimed solely at LLMs is not currently appropriate: the technology is too new, the uncertainties too high and the risk of inadvertently stifling innovation too great. Broader legislation on AI governance may emerge in future, though this …
Report: Government response published
#36 —
Recommendation: The Department for Health and Social Care should work with NHS bodies to ensure future proof data protection provisions are embedded in licensing terms. This would help reassure patients given the possibility of LLM businesses working with NHS data being …
Report: Government response published
#35 —
Recommendation: Further clarity on data protection law is needed. The Information Commissioner ’s Office should work with DSIT to provide clear guidance on how data protection law applies to the complexity of LLM processes, including the extent to which individuals can …
Report: Government response published
#34 —
Recommendation: The AI Safety Institute should develop robust techniques to identify and mitigate societal risks. The Government’ s AI risk register should include a range of societal risks, developed in consultation with civil society. DSIT should also use its White Paper …
Report: Government response published
#33 —
Recommendation: LLMs may amplify numerous existing societal problems and are particularly prone to discrimination and bias. The economic impetus to use them before adequate guardrails have been developed risks deepening inequality.
Report: Government response published
#31 —
Recommendation: The AI Safety Institute should develop new ways to identify and track models once released, standardise expectations of documentation, and review the extent to which it is safe for some types of model to publish the underlying software code, weights …
Report: Government response published
#30 —
Recommendation: There is a credible security risk from the rapid and uncontrollable proliferation of highly capable openly available models which may be misused or malfunction. Banning them entirely would be disproportionate and likely ineffective. But a concerted effort is needed to …
Report: Government response published
#29 —
Recommendation: The AI Safety Institute should publish an assessment of engineering pathways to catastrophic risk and warning indicators as an immediate priority. It should then set out plans for developing scalable mitigations. (We set out recommendations on powers and take-down requirements …
Report: Government response published
#28 —
Recommendation: There are however no warning indicators for a rapid and uncontrollable escalation of capabilities resulting in catastrophic risk. There is no cause for panic, but the implications of this intelligence blind spot deserve sober consideration.
Report: Government response published
#26 —
Recommendation: The Government should publish an AI risk taxonomy and risk register. It would be helpful for this to be aligned with the National Security Risk Assessment.
Report: Government response published
#25 —
Recommendation: The Government has made welcome progress on understanding AI risks and catalysing international co-operation. There is however no publicly agreed assessment framework and shared terminology is limited. It is therefore difficult to judge the magnitude of the issues and priorities.
Report: Government response published
#22 —
Recommendation: We recommend that the Government explores the options for and feasibility of acquiring a sovereign LLM capability. No option is risk free, though commissioning external developers might work best. Any public sector capability would need to be designed to the …
Report: Government response published
#20 —
Recommendation: A sovereign UK LLM capability could deliver substantial value if challenges around reliability, ethics, security and interpretability can be resolved. LLMs could in future benefit central departments and public services for example, though it remains too early to consider using …
Report: Government response published
#13 —
Recommendation: The Government is not striking the right balance between innovation and risk. We appreciate that recent advances have required rapid security evaluations and we commend the AI Safety Summit as a significant achievement. But Government attention is shifting too far …
Report: Government response published
#10 —
Recommendation: Large language models have significant potential to benefit the economy and society if they are developed and deployed responsibly. The UK must not lose out on these opportunities.
Report: Government response published
#1 —
Recommendation: Large language models (LLMs) will have impacts comparable to the invention of the internet.
Report: Government response published
#28 —
Recommendation: Any proposal for a new text and data mining regime must include transparency mechanisms that enable rightsholders to check whether their data has been used. It must offer technical enforceability that goes beyond the likes of robots.txt indicators, which remain …
Report: Government response published
#27 —
Recommendation: While we welcome the new Government’s desire to make progress on this issue, we caution strongly against adopting a flawed opt-out regime comparable to the version operating in the EU. Much better means for ensuring technical viability, transparency, consent and …
Report: Government response published
#26 —
Recommendation: But that does not mean the Government should pursue rules that primarily benefit foreign tech firms (who seem prepared to pay vast sums on energy, computing facilities and staff—but not on data). Previous efforts to find a solution have been …
Report: Government response published
#25 —
Recommendation: The use of news content to train generative AI has the potential to reshape the economics of the media industry. The UK needs a better framework for governing how this works. There are arguments for and against tougher rules. On …
Report: Government response published
#20 —
Recommendation: Advances in generative AI are enabling tech firms to provide engaging and high quality news summaries. This suggests they are increasingly acting as publishers and may need to be regulated as such. Ofcom’s media plurality framework is rapidly becoming outdated, …
Report: Government response published
#19 —
Recommendation: The Government should give Ofcom the necessary powers to investigate tech firm recommender algorithms and the operations of large language models (LLMs). This will become increasingly important amid concerns about the potential for political influence and bias in LLMs.
Report: Government response published
#18 —
Recommendation: More transparency over tech platforms’ algorithms is needed (even if transparency on its own will not be enough). Empowering the regulators quickly is necessary and achievable.
Report: Government response published
#32 —
Recommendation: To improve coherence between regulators and departments, we recommend that government departments such as the Department for Science, Innovation and Technology, regulators such as those participating in the Digital Regulation Cooperation Forum, and bodies such as the British Standards Institute …
Report: Government response published
#24 —
Recommendation: The Government should make every effort to maintain a regular dialogue with counterparts on regulation and legislation on a variety of issues affecting digital trade. These might include regulation of emerging technologies, approaches to data, or digitisation of trade documents. …
Report: Government response published
#22 —
Recommendation: The governance of artificial intelligence and its impact on our society and economy is still in its infancy. Regulation of artificial intelligence cannot be undertaken in isolation, and should be considered in cooperation with our global partners.
Report: Government response published
HSSIB safety recommendations(2)
Missed detection of lung cancer on chest X-rays of patients being seen in primary care
HSIB recommends that NHSX, in collaboration with relevant stakeholders such as The Royal College of Radiologists and The Society and College of Radiographers, develops guidance to support independent benchmarking and validation of artificial intelligence algorithms for the identification of lung …
Safety Recommendation
12-lead electrocardiograms (ECGs) in ambulance services
Algorithm developers can improve patient safety by collecting data from different ethnic groups across different geographical locations to help increase the global representation and accuracy of auto-interpretation algorithms for STEMI.
Safety Observation
NAO audit recommendations(8)
Use of artificial intelligence in government
CDDO should work with the government functions to review existing guidance, government standards and assurance processes to ensure they adequately address the opportunities and risks of AI use and provide sufficient levers to promote safe and responsible use of AI …
Partially accepted
Use of artificial intelligence in government
CDDO should work with the government functions to review existing guidance, government standards and assurance processes to ensure they adequately address the opportunities and risks of AI use and provide sufficient levers to promote safe and responsible use of AI …
Partially accepted
Use of artificial intelligence in government
CDDO should work with the government functions to review existing guidance, government standards and assurance processes to ensure they adequately address the opportunities and risks of AI use and provide sufficient levers to promote safe and responsible use of AI …
Partially accepted
Use of artificial intelligence in government
CDDO should work with the government functions to review existing guidance, government standards and assurance processes to ensure they adequately address the opportunities and risks of AI use and provide sufficient levers to promote safe and responsible use of AI …
Accepted
Use of artificial intelligence in government
CDDO should work with the government functions to review existing guidance, government standards and assurance processes to ensure they adequately address the opportunities and risks of AI use and provide sufficient levers to promote safe and responsible use of AI …
Accepted
Use of artificial intelligence in government
To deliver on its strategy for public sector AI adoption, the Cabinet Office should: In collaboration with DSIT, assess the new strategy and governance arrangements to make sure they are fit for purpose and ensure effective coordination with DSIT-led AI …
Partially accepted
Use of artificial intelligence in government
CDDO should continue to prioritise the 2022-2025 roadmap for digital and data, to address the legacy IT infrastructure and data quality and access barriers to adoption of AI, and ensure future plans maintain continued focus on addressing the risks that …
Accepted
Use of artificial intelligence in government
The Cabinet Office should establish how government can bring together and share accessible insights from cross-government activity to identify, prioritise and test scalable AI opportunities in the public sector, including working with DSIT to leverage the wider research landscape such …
Partially accepted
Independent reviews(10)
National workload action group: reports on social worker workload — Rec 17
DfE should urgently produce national guidance on the use of AI in children’s social care, in partnership with Ofsted, Social Work England, BASW, Unison and the new AI Safety Institute, building on existing frameworks and standards to provide an ethical framework for decision-making to: • underpin the increased use of …
Health & Social Care
Fisher Review (Part 1) — Rec 2
To support the wider use of advanced technology in the criminal justice system, a cross-agency protocol should be created, covering the ethical and appropriate use of artificial intelligence in the analysis and disclosure of investigative material.
Justice & Legal
Accepted
Hall-Pesenti AI Review — Rec 14
The Information Commissioner's Office and the Alan Turing Institute should develop a framework for explaining processes, services and decisions delivered by AI, to improve transparency and accountability.
Other
Furman Review — Rec 20
Strategic recommendation D: The government, CMA and the Centre for Data Ethics and Innovation should continue to monitor how use of machine learning algorithms and artificial intelligence evolves to ensure it does not lead to anti-competitive activity or consumer detriment, in particular to vulnerable consumers.
Other
Sewell Commission — Rec 3
Improve the transparency and use of artificial intelligence The Commission supports the recommendations of the Centre for Data Ethics and Innovation (CDEI) and calls on the government to: • place a mandatory transparency obligation on all public sector organisations applying algorithms that have an impact on significant decisions affecting individuals …
Other
Goldacre Review — Rec TRE 57
Address TREs for Artificial Intelligence, but as a separate workstream, funded by existing AI resource TREs with the core features described above will readily support all analysis using traditional analytical or epidemiological research techniques. Analysis and research involving techniques that fall under the heading of Artificial Intelligence – particularly unsupervised …
Health & Social Care
Penrose Competition Review — Rec 13
NDMU should have a legal duty to extend and promote competition in the monopolies it regulates, by making pro-competition interventions to reinstate normal competitive conditions wherever it's possible and proportionate. As the Furman Review also recommended, this should include: designing and enforcing a pro-competitive code of conduct to give both …
Other
Penrose Competition Review — Rec 12
the new digital unit's extra-strong upfront powers must be ring-fenced tightly, to prevent regulatory creep, otherwise they will steadily spread to cover every digital sector of the economy. To reinforce this vital, central point, the new unit should be called the Network & Data Monopolies Unit (NDMU) and its extra-strong …
Other
Gloster Report (LCF/FCA) — Rec 13
the Treasury and other relevant Government bodies should work with the FCA to ensure that the legislative framework enables the FCA to intervene promptly and effectively in the marketing and sale through technology platforms, and unregulated intermediaries, of speculative illiquid securities and similar retail products.
Other
Goldacre Review — Rec IG 24
Negotiate co-ownership of claimed commercial innovations from NHS data When a company develops an “algorithm” such as a risk prediction tool they typically apply existing tools, techniques and code libraries to huge, richly detailed health datasets that were collected at great cost. The code libraries used for this work, such …
Health & Social Care