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YouTube Algorithms Allegedly Amplify Eating Disorder Content to Adolescent Girls

1 news report Synced from source · record last edited 5 Sep 2026
On this page 10 sections

In brief

An AI system built and deployed by YouTube and Google allegedly harmed YouTube users, Women and girls and 3 others.

Risk domain
Discrimination and Toxicity Exposure to toxic content
Occurred
Coverage
1 reportDec 2024

What happened

YouTube's recommendation algorithm has allegedly been directing teen users to harmful content promoting eating disorders and self-harm, according to a study by the Center for Countering Digital Hate. Almost 70% of the recommended videos in searches related to dieting or weight loss reportedly contained content likely to exacerbate body image anxieties.

Editor's notes

The full Center for Countering Digital Hate report is accessible at https://counterhate.com/wp-content/uploads/2024/12/CCDH.YoutubeED.Nov24.Report_FINAL.pdf.

Laws on record for this risk area

Policy angle: Classified under Discrimination and Toxicity (Exposure to toxic content) in the MIT AI Risk Repository taxonomy; 4 instruments on record cover this risk area.

Matched from the record's risk category and country to the instruments in effect recorded here: a law listed covers the area, which is not a finding that it applies to this incident. A reviewer can correct the match in the repository (data/external/incident_overrides.yaml).

News reports (1)

Titles link to the original publisher; report text is not reproduced here.

  1. YouTube algorithms consistently push eating disorder and self-harm content to teen girls, new study finds
    cbsnews.com · Elizabeth Germino

Who was involved

Alleged deployer
YouTube Google
Alleged developer
YouTube Google
Alleged harmed party
YouTube users Women and girls Girls Emotionally vulnerable individuals Adolescent girls

AI systems implicated

YouTube recommendation systemYouTubeRecommender systemsContent recommendation systems

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)

  • 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...

    Future Risks of Frontier AI (GOS2023)

  • 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)

Linked by editors or by text similarity in the source dataset.

Incidents in the same risk subdomain

All incidents in this subdomain

Other incidents involving YouTube

Source record: incident #873 on the AI Incident Database · all 1 report