AIPolicyTracker

AI incident ·

Alexa Recommended Dangerous TikTok Challenge to Ten-Year-Old Girl

3 news reports Snapshot 7 Sep 2026

In brief

An AI system built and deployed by Amazon allegedly harmed Kristin Livdahl'S Daughter, Amazon Echo Customers and 1 other.

Risk domain
Discrimination and Toxicity Exposure to toxic content
Occurred
Coverage
3 reportsDec 2021

What happened

Amazon’s voice assistant Alexa suggested “the penny challenge,” which involves dangerously touching a coin to the prongs of a half-exposed plug, when a ten-year-old girl asked for a challenge to do.

Laws that address this harm

Policy angle: Classified under Discrimination and Toxicity (Exposure to toxic content) in the MIT AI Risk Repository taxonomy; 5 recorded instruments address this use case in the United States.

Matched from the record's risk domain and country to the instruments recorded here. A reviewer can correct the match in the repository (data/external/incident_overrides.yaml).

News reports (2)

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

Who was involved

Alleged deployer
Amazon
Alleged developer
Amazon
Alleged harmed party
Kristin Livdahl'S Daughter, Amazon Echo Customers, Children Using Alexa

Classification (MIT AI Risk Repository taxonomy)

Causal entity
AI
Intent
Unintentional
Timing
Post-deployment
Harm level
AI tangible harm issue
Sectors
information and communication
Countries
US

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)

  • 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)

  • 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)

  • 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)

Incidents in the same risk subdomain

All incidents in this subdomain

Other incidents involving Amazon

Source record: incident #160 on the AI Incident Database · all 3 reports