AI incident ·
Facebook Allegedly Failed to Police Anti-Rohingya Hate Speech Content That Contributed to Violence in Myanmar
In brief
An AI system built and deployed by Facebook and Meta allegedly harmed Rohingya People, Rohingya Facebook Users and 3 others.
- Risk domain
- Discrimination and Toxicity
- Occurred
- Coverage
- 5 reports
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.
Laws that address this harm
Policy angle: Classified under Discrimination and Toxicity (Exposure to toxic content) in the MIT AI Risk Repository taxonomy; 5 recorded instruments address this use case.
- Colorado AI Act
- India DPDP Act
- Law No. 132/2025 on artificial intelligence
- NYC Local Law 144 (automated employment decision tools)
- EU AI Act
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 (5)
Titles link to the original publisher; report text is not reproduced here.
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)
- Risk domain
- Discrimination and Toxicity
- Risk subdomain
- 1.2 Exposure to toxic content
- 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"
- Toxicity
"Toxicity means the generated content contains rude, disrespectful, and even illegal information"
- 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...
- Not-Suitable-for-Work (NSFW) Prompts
"Inputting a prompt contain an unsafe topic (e.g., notsuitable-for-work (NSFW) content) by a benign user. "
- 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...
- Toxicity and Abusive Content
This typically refers to rude, harmful, or inappropriate expressions.
- Harmful responses
"Current Frontier AI mdoels amplify existing biases within their training data and can be manipulated into providing potentially harmful responses, for example abusive language or discriminatory responses91,92. This is n...
- 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...
Incidents in the same risk subdomain
- KBS AI Translation Subtitles Reportedly Broadcast Profanity During Artemis II Launch Livestream
- Grok Allegedly Generated Publicly Visible Sexist Abuse Targeting Swiss Finance Minister Karin Keller-Sutter After X User Prompt
- Trump Reportedly Posted Purportedly AI-Generated Racist Video Depicting Barack and Michelle Obama as Apes on Truth Social
- Tencent's WeChat-Integrated Yuanbao Chatbot Reportedly Insulted User During Coding Debug Request
- Alleged Harmful Outputs and Data Exposure in Children's AI Products by FoloToy, Miko, and Character.AI
- AI Training Dataset for Detecting Nudity Allegedly Found to Contain CSAM Images of Identified Victims
Other incidents involving Facebook, Meta
Source record: incident #169 on the AI Incident Database · all 5 reports