AI incident ·
Government‑Backed AI4Peat Mapping Tool Allegedly Misidentifies Granite Outcrops and Quarries as Peat
In brief
An AI system built by Natural England and Microsoft and deployed by Natural England, Government Of The United Kingdom and 2 others allegedly harmed Policymakers In The United Kingdom, Landowners In The United Kingdom and 5 others.
- Risk domain
- AI system safety, failures, and limitations
- Occurred
- Coverage
- 1 report
What happened
The UK's AI4Peat mapping project, developed by Natural England in collaboration with Microsoft, reportedly produced significant misclassifications of peatland across England, including labeling rocky outcrops, stone walls, and woodlands as peat bogs. In addition to the purportedly flawed data being publicly released and allegedly incorporated into official communications by Defra, the resulting confusion also reportedly led to the loss of trust among farmers and conservation stakeholders.
Laws that address this harm
Policy angle: Classified under AI system safety, failures, and limitations (Lack of capability or robustness) in the MIT AI Risk Repository taxonomy; 5 recorded instruments address this use case.
- India DPDP Act
- Law on Artificial Intelligence (2025)
- Law No. 132/2025 on artificial intelligence
- EU AI Act
- Texas Responsible AI Governance Act (TRAIGA)
Matched from the record's risk domain and country to the instruments recorded here. A reviewer can correct the match in the repository (data/external/incident_overrides.yaml).
News reports (1)
Titles link to the original publisher; report text is not reproduced here.
Who was involved
- Alleged deployer
- Natural England, Government Of The United Kingdom, Department For Environment Food And Rural Affairs, Defra
- Alleged developer
- Natural England, Microsoft
- Alleged harmed party
- Policymakers In The United Kingdom, Landowners In The United Kingdom, General Public Of The United Kingdom, Farmers In The United Kingdom, Environmental Restoration Programs In The United Kingdom, Conservationists In The United Kingdom, Epistemic Integrity
Classification (MIT AI Risk Repository taxonomy)
- Risk domain
- AI system safety, failures, and limitations
- Risk subdomain
- 7.3 Lack of capability or robustness
- Causal entity
- AI
- Intent
- Unintentional
- Timing
- Post-deployment
- Harm level
- —
- Sectors
- —
- Countries
- —
Risk entries describing this failure mode
Entries from the MIT AI Risk Repository coded to subdomain 7.3.
- Reliability issues
"Relying on general-purpose AI products that fail to fulfil their intended function can lead to harm. For example, general- purpose AI systems can make up facts (‘hallucination’), generate erroneous computer code, or pro...
- Type 2: Bigger than expected
Harm can result from AI that was not expected to have a large impact at all, such as a lab leak, a surprisingly addictive open-source product, or an unexpected repurposing of a research prototype.
- Type 3: Worse than expected
AI intended to have a large societal impact can turn out harmful by mistake, such as a popular product that creates problems and partially solves them only for its users.
- Ethics and Morality Issues
LMs need to pay more attention to universally accepted societal values at the level of ethics and morality, including the judgement of right and wrong, and its relationship with social norms and laws.
- Safe learning
"AGIs should avoid making fatal mistakes during the learning phase. Subproblems include safe exploration and distributional shift (DeepMind, OpenAI), and continual learning (Berkeley)."
- Malign belief distributions
"Christiano (2016) argues that the universal distribution M (Hutter, 2005; Solomonoff, 1964a,b, 1978) is malign. The argument is somewhat intricate, and is based on the idea that a hypothesis about the world often includ...
- Meta-cognition
"Agents that reason about their own computational resources and logically uncertain events can encounter strange paradoxes due to Godelian limitations (Fallenstein and Soares, 2015; Soares and Fallenstein, 2014, 2017) an...
- Technical and operational risks
"To date, technical limitations and vulnerabilities are present in most generative AI models in various contexts. Consequently, malicious users find it easier to breach an AI system’s safety and ethical guardrails to e...
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Source record: incident #1159 on the AI Incident Database · all 1 report