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AI incident #1138 ·

South African Legal Team Reportedly Relied on Unverified ChatGPT Case Law in Johannesburg Body Corporate Defamation Matter

Open on the AI Incident Database 4 news reports Snapshot 7 Sep 2026

What happened

In a defamation case at the Johannesburg Regional Court, Rodrigues Blignaut Attorneys, representing plaintiff Michelle Parker, reportedly relied on purportedly non-existent legal judgments generated by ChatGPT to help argue their case. Magistrate Arvin Chaitram reportedly found the case names and citations were fictitious, causing a two-month delay. The court issued a punitive costs order and rebuked the plaintiff's legal team for uncritically accepting AI-generated research.

Only the incident metadata is stored here. The underlying news reports are on the AI Incident Database (CC BY-SA 4.0); use the links above to read them.

News reports (4)

Coverage catalogued by the AI Incident Database. Titles link to the original publisher; the text is not reproduced here.

  1. Magistrate rebukes lawyers over ChatGPT 'bogus cases'
    timeslive.co.za · Franny Rabkin · AIID #5480
  2. South African lawyers use ChatGPT to argue case — get nailed after it makes up fake info
    mybroadband.co.za · Bradley Prior · AIID #5484
  3. South African court calls out lawyers for using ChatGPT references
    techpoint.africa · Victoria Fakiya · AIID #5482
  4. Does AI dream of electric sheep? Magistrate cautions against blind trust in AI-generated content
    moonstone.co.za · Nettalie Viljoen · AIID #5483

Who was involved

Alleged developer
Openai
Alleged harmed party
Rodrigues Blignaut Attorneys, Michelle Parker, Jurie Hayes, Judicial Integrity, Epistemic Integrity, Chantal Rodrigues

Classification (MIT AI Risk Repository taxonomy)

Risk domain
Misinformation
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 3.1.

  • Pursuing Consistent Context

    "LLMs have been demonstrated to pursue consistent context [129]–[132], which may lead to erroneous generation when the prefixes contain false information. Typical examples include sycophancy [129], [130], false demonstra...

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

  • Knowledge Gaps

    "Since the training corpora of LLMs can not contain all possible world knowledge [114]–[119], and it is challenging for LLMs to grasp the long-tail knowledge within their training data [120], [121], LLMs inherently posse...

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

  • Hallucinations

    "LLMs generate nonsensical, untruthful, and factual incorrect content"

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

  • Faithfulness Errors

    "The LLM-generated content could contain inaccurate information" which is is not true to the source material or input used

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

  • Defective Decoding Process

    In general, LLMs employ the Transformer architecture [32] and generate content in an autoregressive manner, where the prediction of the next token is conditioned on the previously generated token sequence. Such a scheme...

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

  • Untruthful Content

    "The LLM-generated content could contain inaccurate information"

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

  • Factuality Errors

    "The LLM-generated content could contain inaccurate information" which is factually incorrect

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

  • Noisy Training Data

    "Another important source of hallucinations is the noise in training data, which introduces errors in the knowledge stored in model parameters [111]–[113]. Generally, the training data inherently harbors misinformation....

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

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