Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Understanding Generative AI Risks for Youth: A Taxonomy Based on Empirical Data
1
Zitationen
5
Autoren
2025
Jahr
Abstract
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.
Ähnliche Arbeiten
Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
2017 · 20.811 Zit.
Generative Adversarial Nets
2023 · 19.896 Zit.
Visualizing and Understanding Convolutional Networks
2014 · 15.336 Zit.
"Why Should I Trust You?"
2016 · 14.615 Zit.
Generative adversarial networks
2020 · 13.228 Zit.