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AI incident #1125 ·

Reported Use and Circulation of AI-Generated Misinformation and Fake Victim Visuals After Air India 171 Crash

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

What happened

Following the June 12, 2025 crash of Air India Flight 171, bad-faith actors reportedly used generative AI to produce fake videos and images misrepresenting victims and dramatizing the event. These AI-generated fakes spread widely on social media during the investigation delay, reportedly confusing even aviation professionals and misleading the public. Experts warned that the misuse exploited the grief of victims' families and amplified misinformation about the tragedy.

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. Real grief, fake images: How AI hijacked Air India crash coverage
    asia.nikkei.com · Viren Naidu · AIID #5419
  2. AI-generated content fuels misinformation after Air India crash
    economictimes.indiatimes.com · Arindam Majumder, Himanshi Lohchab · AIID #5418
  3. Air India plane crash: AI-generated fake reports, videos spreading misinformation; fraudsters exploiting vulnerability
    timesofindia.indiatimes.com · TOI Business Desk · AIID #6193
  4. AI-Generated Misinformation Over Air India Crash Confuses Even Expert Aviation Professionals
    in.mashable.com · Mashable News Staff · AIID #5417

Who was involved

Alleged developer
Generative Ai Developers
Alleged harmed party
General Public, Families Of The Victims Of Air India Flight 171, Experts Investigating The Air India Flight 171 Crash, Epistemic Integrity

Classification (MIT AI Risk Repository taxonomy)

Risk domain
Misinformation
Causal entity
Human
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)

  • 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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