OOF-AII-482616

Crown Prosecution Service Reportedly Filed Purportedly AI-Generated False Citations in Two Extradition Appeals

In joined appeals involving Andreea-Maria Tobosaru and Marian Tofan, the Crown Prosecution Service reportedly filed grounds of opposition containing two nonexistent authorities that likely originated from purported generative AI. The citations also appeared in a later document. The High Court said they did not affect the appeals, and the CPS apologized, citing inadequate verification by a reviewing lawyer.

Event date
2025-06-03
Country
Location not specified
Severity
Low
Evidence state
Single-source

Assessment status

Generated from available public incident evidence using OOF® governance methodology. It is not an official investigation, legal determination, regulatory finding, certification, or OOF® Approved Assessment.

View source record from AI Incident Database (Responsible AI Collaborative)

Assessment confidence dimensions

Source corroboration
Single-source

Measures source-group corroboration only; it does not establish factual truth.

Extraction completeness
Moderate

Measures populated source fields after deterministic normalization; it does not measure correctness.

Methodological assignment confidence
Limited

Measures automated assignment support; substantive correctness requires authorized human review.

Known facts from the incident record

  • The incident record identifies the event date as 2025-06-03.

Reported claims

  • In joined appeals involving Andreea-Maria Tobosaru and Marian Tofan, the Crown Prosecution Service reportedly filed grounds of opposition containing two nonexistent authorities that likely originated from purported generative AI. The citations also appeared in a later document. The High Court said they did not affect the appeals, and the CPS apologized, citing inadequate verification by a reviewing lawyer.

Material unknowns

  • Independent corroboration is not established in the available incident record.

Primary governance problem

The incident appears to expose a governance problem involving models, affect. Available reporting supports preliminary methodological interpretation, not a finding of fault or non-compliance.

Architecture intelligence

PGA™ — Prediction Governance Architecture

Primary architecture

The reported models, affect concern falls within the governed questions represented by PGA™ — Prediction Governance Architecture.

Governance focus: How should models, affect be governed in the conditions described by the available incident evidence?

Deeper methodology is withheld because the available evidence does not justify a Parent Standard or Core Module.

Preliminary governance questions

  • How should models, affect be governed in the conditions described by the available incident evidence?
  • What operational authority and controls applied to Crown Prosecution Service, Lawyers at the relevant time?
  • What evidence would establish how Large Language Models, Ai Assisted Legal Research Systems produced or enabled the reported outcome?
  • What additional independent evidence is required to resolve the material evidence gaps?

Evidence gaps and limitations

  • Only one independent source group is represented; corroboration is not established.
  • Country could not be resolved from the available evidence.
  • AI technology is not specified in the source record.
  • industry is not specified in the source record.
  • AI system type is not specified in the source record.

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