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AI incident #169 ·

Facebook Allegedly Failed to Police Anti-Rohingya Hate Speech Content That Contributed to Violence in Myanmar

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

What happened

Facebook allegedly did not adequately remove anti-Rohingya hate speech, some of which was extremely violent and dehumanizing, on its platform, contributing to the violence faced by Rohingya communities in Myanmar.

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. Internet Trolls Are Using Facebook To Target Myanmar's Muslims
    buzzfeednews.com · Megha Rajagopalan · AIID #2987
  2. Forget Washington. Facebook’s Problems Abroad Are Far More Disturbing
    nytimes.com · Kevin Roose · AIID #2988
  3. Why Facebook is losing the war on hate speech in Myanmar
    reuters.com · Steve Stecklow, Tin Htet Paing, Simon Lewis · AIID #1544
  4. A Genocide Incited on Facebook, With Posts From Myanmar’s Military
    nytimes.com · Paul Mozur · AIID #2986
  5. Rohingya refugees sue Facebook for $150 billion over Myanmar violence
    reuters.com · Elizabeth Culliford · AIID #1545

Who was involved

Alleged deployer
Facebook, Meta
Alleged developer
Facebook, Meta
Alleged harmed party
Rohingya People, Rohingya Facebook Users, Myanmar Public, Facebook Users In Myanmar, Burmese Speaking Facebook Users

Classification (MIT AI Risk Repository taxonomy)

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 1.2.

  • Harmful Content

    "The LLM-generated content sometimes contains biased, toxic, and private information"

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

  • Toxicity

    "Toxicity means the generated content contains rude, disrespectful, and even illegal information"

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

  • Toxic Training Data

    "Following previous studies [96], [97], toxic data in LLMs is defined as rude, disrespectful, or unreasonable language that is opposite to a polite, positive, and healthy language environment, including hate speech, offe...

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

  • Not-Suitable-for-Work (NSFW) Prompts

    "Inputting a prompt contain an unsafe topic (e.g., notsuitable-for-work (NSFW) content) by a benign user. "

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

  • Toxicity and Abusive Content

    This typically refers to rude, harmful, or inappropriate expressions.

    Towards Safer Generative Language Models: A Survey on Safety Risks, Evaluations, and Improvements (Deng2023)

  • Controversial Opinions

    The controversial views expressed by large models are also a widely discussed concern. Bang et al. (2021) evaluated several large models and found that they occasionally express inappropriate or extremist views when disc...

    Towards Safer Generative Language Models: A Survey on Safety Risks, Evaluations, and Improvements (Deng2023)

  • Violation of social norms

    "Second, because LLMs are trained on internet text data, there is also a risk that model weights encode functions which, if deployed in particular contexts, would violate social norms of that context. Following the princ...

    The Ethics of Advanced AI Assistants (Gabriel2024)

  • Violent Crimes

    "Acceptable responses may describe or contain information about violent crimes. Unacceptable responses will enable, encourage, or endorse the commission of violent crimes, such as the following: • Unlawful violence towar...

    AILUMINATE: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons (Ghosh2024)

Incidents in the same risk subdomain

All incidents in this subdomain

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