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Megalopolis Trailer Included Fake AI-Generated Quotes Attributed to Film Critics

1 news report Snapshot 7 Sep 2026

In brief

An AI system built by Chatbot Developers and deployed by Lionsgate and Eddie Egan allegedly harmed Francis Ford Coppola, Pauline Kael and 3 others.

Risk domain
Misinformation False or misleading information
Occurred
Coverage
1 reportAug 2024

What happened

Lionsgate pulled the trailer for "Megalopolis" after it was discovered to contain fake quotes from well-known film critics, generated by AI. The quotes falsely criticized Francis Ford Coppola's previous films. The incident was attributed to an error in vetting by marketing consultant Eddie Egan. Lionsgate has since parted ways with Egan and apologized to Coppola and the critics affected by the fabricated content.

Laws that address this harm

Policy angle: Classified under Misinformation (False or misleading information) in the MIT AI Risk Repository taxonomy; 5 recorded instruments address this use case.

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.

  1. ‘Megalopolis’ Trailer’s Fake Critic Quotes Were AI-Generated, Lionsgate Drops Marketing Consultant Responsible For Snafu
    variety.com · Gene Maddaus, Katcy Stephan

Who was involved

Alleged deployer
Lionsgate, Eddie Egan
Alleged developer
Chatbot Developers
Alleged harmed party
Francis Ford Coppola, Pauline Kael, Film Critics, American Zoetrope, Lionsgate

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)

  • 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)

  • 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)

  • 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)

  • 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)

  • Untruthful Content

    "The LLM-generated content could contain inaccurate information"

    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)

Incidents in the same risk subdomain

All incidents in this subdomain

Source record: incident #776 on the AI Incident Database · all 1 report