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Purported AI-Generated Video Reportedly Depicts Trump Criticizing Former Kenyan Deputy President Rigathi Gachagua

1 news report Snapshot 7 Sep 2026

In brief

An AI system built by Deepfake Technology Developers and Synthetic Audio Generation Technology Developers and deployed by Unknown allegedly harmed Rigathi Gachagua, Donald Trump and 3 others.

Risk domain
Misinformation False or misleading information
Occurred
Coverage
1 reportFeb 2025

What happened

In late January and early February 2025, a video circulated on Facebook purporting to show U.S. President Donald Trump calling former Kenyan deputy president Rigathi Gachagua a "criminal" and criticizing his alleged misuse of AI. However, fact-checkers have reported the video was AI-generated. The footage appears to have been manipulated from Trump's inauguration speech on January 20, 2025, with reported mismatched lip movements and audio overlays.

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. US president Trump didn’t call former Kenyan deputy president Gachagua a ‘terrible guy’ and a ‘criminal’ – viral video manipulated
    africacheck.org · Dancan Bwire

Who was involved

Alleged deployer
Unknown
Alleged harmed party
Rigathi Gachagua, Donald Trump, General Public Of Kenya, United States Kenya Relations, Kenyan Political Discourse

Classification (MIT AI Risk Repository taxonomy)

Risk domain
Misinformation
Causal entity
AI
Intent
Intentional
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

Other incidents involving Unknown

Source record: incident #1052 on the AI Incident Database · all 1 report