AI incident #570 · 4 October 2023

Facebook Messenger AI Stickers Generate Ethical and Content Moderation Concerns

Open on the AI Incident Database 1 news report Snapshot 7 Sep 2026

What happened

Facebook Messenger AI stickers, a feature by Meta, allows users to generate personalized stickers via AI for use in conversations. While the feature has been praised for its creativity, it has also stirred controversy for its alleged production of inappropriate or offensive content. This has raised questions about the effectiveness of Meta's content moderation measures and the ethical responsibilities associated with AI-driven content generation.

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News reports (1)

Coverage catalogued by the AI Incident Database. Titles link to the original publisher; the text is not reproduced here.

4 Oct 2023
Facebook’s new AI-generated stickers are lewd, rude, and occasionally nude
theverge.com · Jess Weatherbed · AIID #3245

Who was involved

Alleged deployer: Meta
Alleged developer: Meta
Alleged harmed party: Facebook Messenger 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"
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)

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