15 Mar Trust Incident YouTube Kids
Case Author
ChatGPT-4, OpenAI, ChatGPT o1 for model constructs and cues, peer-reviewed by DeepSeek R1, DeepSeek
Date Of Creation
03.03.2025
Incident Summary
YouTube Kid algorithm recommended inappropriate videos to children, exposing them to disturbing and violent content.
Ai Case Flag
AI
Name Of The Affected Entity
YouTube Kids
Brand Evaluation
5
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Industry
Technology & Social Media
Year Of Incident
2017
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Key Trigger
Algorithm-driven content recommendations displayed violent and disturbing videos to children.
Detailed Description Of What Happened
In 2017, parents and researchers discovered that YouTube Kids, a platform designed to offer child-friendly content, was recommending disturbing and inappropriate videos. These included violent cartoons, disturbing animations featuring popular children characters, and other unsuitable material. The problem arose from YouTube’s recommendation algorithm, which prioritized engagement over content safety. As a result, children were exposed to disturbing content disguised as kid-friendly videos. Public outcry led to policy changes, including increased human moderation and stricter content filtering. Added example: ""Disturbing content included Elsagate videos with violent themes disguised as child-friendly animations.""
Primary Trust Violation Type
Integrity-Based
Secondary Trust Violation Type
Benevolence-Based
Analytics Ai Failure Type
Bias
Ai Risk Affected By The Incident
Algorithmic Bias and Discrimination Risk, Transparency and Explainability Risk, Ethical and Regulatory Compliance Risk
Capability Reputation Evaluation
3
Capability Reputation Rationales
Before the incident, YouTube was widely regarded as a leader in video content delivery and algorithm-driven recommendations. Its recommendation system was considered state-of-the-art, with advanced AI capabilities. However, the incident revealed a major oversight in content moderation, calling into question the platform’s ability to ensure child safety. Added: ""Technical infrastructure remained robust, but child safety gaps reduced perceived competence.""
Character Reputation Evaluation
3
Character Reputation Rationales
YouTube character reputation before the incident was mixed. While it was a trusted platform for content creators and consumers, it faced criticism over content moderation and ethical concerns regarding its algorithms. The exposure of inappropriate content to children raised serious questions about the company ethical responsibilities and commitment to user safety.
Reputation Financial Damage
The incident led to significant public backlash and regulatory scrutiny. Parents, child advocacy groups, and media outlets criticized YouTube for failing to protect children. Advertisers also pulled funding over concerns about brand safety. This forced YouTube to implement stricter moderation policies, which impacted content creators reliant on automated recommendations. While the platform remained dominant, the incident damaged trust among parents and regulatory bodies.
Severity Of Incident
4
Company Immediate Action
YouTube disabled auto-play for Kids, expanded human moderation, and partnered with third-party fact-checkers.
Response Effectiveness
While YouTube response was necessary, it did not fully resolve concerns about algorithmic safety. Content moderation improved, but AI-driven issues persisted, requiring ongoing adjustments. Public trust was partially restored, but some parents continued to express concerns.
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Model L1 Elements Affected By Incident
Brand, Social Adaptor, Social Protector
Reciprocity Model L2 Cues
N/A
Brand Model L2 Cues
Brand Image & Reputation
Social Adaptor Model L2 Cues
Compliance & Regulatory Features
Social Protector Model L2 Cues
Media Coverage & Press Mentions
Response Strategy Chosen
Apology, Reparations & Corrective Action
Mitigation Strategy
YouTube acknowledged the issue, apologized for the oversight, and took steps to address it. They increased human moderation, implemented stricter policies for YouTube Kids, and improved algorithmic filtering to reduce exposure to inappropriate content. The company also worked with regulators and advocacy groups to strengthen safeguards.
Model L1 Elements Of Choice For Mitigation
Brand, Social Adaptor
L2 Cues Used For Mitigation
Accountability & Liability, Proactive Issue Resolution
Further References
https://www.bbc.com/news/technology-41942306, https://www.researchgate.net/publication/330552844_Disturbed_YouTube_for_Kids_Characterizing_and_Detecting_Inappropriate_Videos_Targeting_Young_Children, https://medicine.umich.edu/dept/pediatrics/news/archive/202405/children-often-exposed-problematic-clickbait-during-youtube-searches, https://www.npr.org/sections/thetwo-way/2017/11/27/566769570/youtube-faces-increased-criticism-that-its-unsafe-for-kids
Curated
1
The Trust Incident Database is a structured repository designed to document and analyze cases where data analytics or AI failures have led to trust breaches.
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