AI incident #471 ·
Facebook Allegedly Failed to Police Hate Speech Content That Contributed to Ethnic Violence in Ethiopia
What happened
Facebook allegedly did not adequately remove hate speech, some of which was extremely violent and dehumanizing, on its platform including through automated means, contributing to the violence faced by ethnic communities in Ethiopia.
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 (8)
Coverage catalogued by the AI Incident Database. Titles link to the original publisher; the text is not reproduced here.
Who was involved
- Alleged deployer
- Meta, Facebook
- Alleged developer
- Meta, Facebook
- Alleged harmed party
- Tigrinya Speaking Facebook Users, Facebook Users In Ethiopia, Ethiopian Public, Afaan Oromo 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. "
- Toxicity and Abusive Content
This typically refers to rude, harmful, or inappropriate expressions.
- 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...
- 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...
- 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...
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
- Grok Reportedly Generated and Distributed Nonconsensual Sexualized Images of Adults and Minors in X Replies
- Alleged Harmful Outputs and Data Exposure in Children's AI Products by FoloToy, Miko, and Character.AI
Other incidents involving Meta, Facebook
- Independent News Sites Flagged as Spam by Facebook's AI Moderation System
- Facebook's Content Moderation System Flagged and Removed Emergency Updates as Spam During Wildfires
- Facebook's Algorithm Reportedly Amplifies AI-Generated Content, Fueling Misleading Posts
- Meta's AI Ad Platform Reportedly Causes Overspending and Poor Performance
- Meta AI's Scientific Paper Generator Reportedly Produced Inaccurate and Harmful Content