UK Government AI Policies and Large Language Models Fact-Fiction Limitations: Analysis & Recommendations

AI has No Mandate for Fact-Checking or Grounding

Commentary created in collaboration with Perplexity AI.

UK Government AI Policies and LLM Fact-Fiction Limitations: Analysis & Recommendations

The UK’s current and planned AI policies prioritize innovation and economic growth but lack robust safeguards to address the fact that large language models (LLMs) cannot intrinsically differentiate factual from fictional content. Below is a critical assessment of existing mitigations and gaps, followed by actionable recommendations.

---

Current Policy Approach to LLM Limitations

1. Pro-Innovation Regulatory Strategy

   • The AI Opportunities Action Plan (2025) and AI Playbook (2025) emphasize sector-specific guidance and voluntary compliance, avoiding prescriptive rules for LLM accuracy.

   • Key Mitigations:

     • Risk Assessments: Public sector buyers must conduct AI risk assessments, but these are not mandated for private developers.

     • GDPR and Accuracy Principle: The ICO’s guidance (2024) states that AI systems need not be 100% accurate but must avoid unfair or harmful inaccuracies. However, this does not address systemic LLM hallucinations.

     • Online Safety Act 2023: Targets illegal/harmful content but excludes general misinformation unless it causes direct harm.

2. Technical and Ethical Guidelines

   • The AI Playbook advises using “appropriate mathematical and statistical procedures” (Recital 71, UK GDPR) but does not prescribe technical solutions (e.g., retrieval-augmented generation) to mitigate hallucinations.

   • The Regulatory Innovation Office (2024) promotes “assurance frameworks” but lacks enforcement mechanisms.

3. Public Sector Deployment

   • The AI Growth Agenda (2025) encourages AI adoption in government services but provides no specific safeguards against LLM-generated misinformation.

---

Critical Gaps

1. No Mandate for Fact-Checking or Grounding

   • Policies do not require LLMs to use verified sources (e.g., legal databases) or implement real-time fact-checking, despite tools like retrieval-augmented generation (RAG) being technically feasible.

2. Overreliance on Post-Hoc Corrections

   • The GDPR’s “right to rectification” is highlighted, but as the Norwegian case demonstrates, rectification is often impractical due to LLMs’ technical limitations.

3. Inadequate Protections for Vulnerable Groups

   • While the Online Safety Act prioritizes child safety, it does not address risks from AI-generated legal or educational misinformation that could mislead minors or caregivers.

4. Lack of Transparency Standards

   • The UK has not adopted EU AI Act-style transparency obligations (e.g., labelling synthetic content), leaving users unaware of LLMs’ propensity for fiction.

---

Recommendations

1. Technical Mitigations

   • Mandate Retrieval-Augmented Generation (RAG): Require public sector AI systems to ground outputs in verified databases (e.g., legislation.gov.uk).

   • Develop Hallucination Detection Tools: Fund research into explainability frameworks (e.g., LIME, SHAP) to trace and correct hallucinations.

2. Regulatory Reforms

   • Amend the Online Safety Act: Expand Section 12 to cover AI-generated legal/educational misinformation posing risks to public trust.

   • Introduce AI Accuracy Standards: Legislate minimum statistical accuracy thresholds for high-risk public sector AI deployments (e.g., healthcare, legal advice).

3. Transparency and Accountability

   • Label Synthetic Content: Require clear disclaimers for AI-generated text, akin to the EU AI Act’s Article 52.

   • Establish an AI Ombudsman: Create an independent body to investigate systemic LLM inaccuracies and enforce corrections.

4. Public Awareness and Education

   • Launch AI Literacy Campaigns: Educate citizens on LLMs’ limitations, focusing on schools and public services.

   • Publish Misinformation Case Studies: Highlight real-world harms (e.g., the Norwegian case) in government guidance to drive accountability.

5. International Collaboration

   • Align with EU AI Act Standards: Adopt transparency and risk-tiering frameworks to ensure cross-border consistency.

   • Push for Global Hallucination Mitigation Protocols: Leverage the UK’s AI Safety Summit legacy to promote technical standards.

---

Conclusion

The UK’s current policies inadequately address LLMs’ inability to distinguish fact from fiction, risking public trust and legal integrity. By mandating technical safeguards, enhancing transparency, and prioritizing accuracy in high-risk domains, the UK can align its pro-innovation agenda with robust protections against AI-generated misinformation.