# Understanding Generative AI Risks for Youth: A Taxonomy Based on Empirical Data

arXiv: https://arxiv.org/abs/2502.16383 · HTML: https://mlsanity.com/paper/2502.16383/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Feb 2025.

## Built on by (most cited first, 7 of 7)

- [Understanding Teen Overreliance on AI Companion Chatbots Through Self-Reported Reddit Narratives](https://mlsanity.com/paper/2507.15783/) (2025)
- [CAREBench: A Child-Safety Risk Benchmark for Language Models](https://mlsanity.com/paper/2606.29685/) (2026)
- [Designing and Evaluating Chain-of-Hints for Scientific Question Answering](https://mlsanity.com/paper/2510.21087/) (2025)
- [Red Teaming LLMs as Socio-Technical Practice: From Exploration and Data Creation to Evaluation](https://mlsanity.com/paper/2602.18483/) (2026)
- [RiskNet: A large-scale dataset of AI risk incidents from news with alignment and multi-dimensional annotations](https://mlsanity.com/paper/2606.08376/) (2026)
- [Responsible Personalisation: The Double-Edged Sword of Personalisation in Human-Robot Interaction](https://mlsanity.com/paper/2607.06344/) (2026)
- [Systemization of Knowledge (SoK): Human-Centered AI Safety for Youth](https://mlsanity.com/paper/2610.08554/) (2026)

## Abstract (arXiv)

Generative AI (GAI) is reshaping the way young users engage with technology. This study introduces a taxonomy of risks associated with youth-GAI interactions, derived from an analysis of 344 chat transcripts between youth and GAI chatbots, 30,305 Reddit discussions concerning youth engagement with these systems, and 153 documented AI-related incidents. We categorize risks into six overarching themes, identifying 84 specific risks, which we further align with four distinct interaction pathways. Our findings highlight emerging concerns, such as risks to mental wellbeing, behavioral and social development, and novel forms of toxicity, privacy breaches, and misuse/exploitation that are not fully addressed in existing frameworks on child online safety or AI risks. By systematically grounding our taxonomy in empirical data, this work offers a structured approach to aiding AI developers, educators, caregivers, and policymakers in comprehending and mitigating risks associated with youth-GAI interactions.