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AI incident #1392 ·

Ars Technica Retracted Article After Purportedly AI-Generated Text Was Presented as Direct Quotes From Matplotlib Maintainer

Open on the AI Incident Database 5 news reports Snapshot 7 Sep 2026

What happened

Ars Technica retracted an article after purportedly AI-generated text was presented as direct quotations from a source who disputed having said them. The editor-in-chief reportedly acknowledged a standards failure and said the publication was not consistent with Ars policy on AI-generated material, and apologized to readers and to matplotlib maintainer Scott Shambaugh. An author reportedly later said he had inadvertently used an AI-paraphrased version of the source text as quotes.

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 (5)

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

  1. Ars Technica Pulls Article With AI Fabricated Quotes About AI Generated Article
    404media.co · Emanuel Maiberg · AIID #6903
  2. Editor’s Note: Retraction of article containing fabricated quotations
    arstechnica.com · Ken Fisher · AIID #6905
  3. Ars Technica Fires Reporter After AI Controversy Involving Fabricated Quotes
    futurism.com · Maggie Harrison Dupré · AIID #7023
  4. When AI Tools Yield Bad Journalism, Who Is Held Accountable?
    jezebel.com · Jim Vorel · AIID #7024
  5. Our newsroom AI policy
    arstechnica.com · Ken Fisher · AIID #7181

Who was involved

Alleged developer
Openai
Alleged harmed party
Scott Shambaugh, Open Source Maintainers, Epistemic Integrity, Ars Technica Readers

Classification (MIT AI Risk Repository taxonomy)

Risk domain
Misinformation
Causal entity
Human
Intent
Unintentional
Timing
Post-deployment
Harm level
—
Sectors
—
Countries
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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)

Incidents in the same risk subdomain

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