AI incident #869 ·

TikTok Algorithms Allegedly Linked to Minors' Exposure to Harmful Content

What happened

Seven French families are suing TikTok, alleging its algorithm exposed minors to harmful content promoting self-harm, eating disorders, and suicide. Two teenagers reportedly died by suicide after viewing such content, while others allegedly attempted suicide or developed mental health issues. The case seeks to establish TikTok's legal liability for failing to protect minors from harmful algorithmic content.

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 (7)

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

  1. French families sue TikTok over alleged promotion of self-harm content
    politico.eu · Océane Herrero, Victor Goury-Laffont · AIID #4398

Who was involved

Alleged deployer
Tiktok
Alleged developer
Tiktok
Alleged harmed party
Tiktok Users, Seven French Families, Minors Using Tiktok

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)

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