title: MinorBench: A hand-built benchmark for content-based risks for children
summary: Large Language Models (LLMs) are rapidly entering children's lives - through
  parent-driven adoption, schools, and peer networks - yet current AI ethics and
  safety research do not adequately address content-related risks specific to
  minors. In this paper, we highlight these gaps with a real-world case study of
  an LLM-based chatbot deployed in a middle school setting, revealing how
  students used and sometimes misused the system. Building on these findings, we
  propose a new taxonomy of content-based risks for minors and introduce
  MinorBench, an open-source benchmark designed to evaluate LLMs on their ability
  to refuse unsafe or inappropriate queries from children. We evaluate six
  prominent LLMs under different system prompts, demonstrating substantial
  variability in their child-safety compliance. Our results inform practical
  steps for more robust, child-focused safety mechanisms and underscore the
  urgency of tailoring AI systems to safeguard young users.
paper_authors._id: 67d3dd208d3b0ca8d5ca7f69
paper_authors.name: Shaun Khoo
paper_authors._id: 67d3dd208d3b0ca8d5ca7f6a
paper_authors.name: Gabriel Chua
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-14T08:55:54.401Z
paper_authors._id: 655f4ff710e5c5fbef30fd97
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/655f4ff710e5c5fbef30fd97/bI_KQnFof50HRqYBWpyu2.jpeg
paper_authors.fullname: Gabriel C
paper_authors.type: user
paper_authors.user: gabrielchua
paper_authors._id: 67d3dd208d3b0ca8d5ca7f6b
paper_authors.name: Rachel Shong
paper_title: MinorBench: A hand-built benchmark for content-based risks for children
paper_summary: Large Language Models (LLMs) are rapidly entering children's lives - through
  parent-driven adoption, schools, and peer networks - yet current AI ethics and
  safety research do not adequately address content-related risks specific to
  minors. In this paper, we highlight these gaps with a real-world case study of
  an LLM-based chatbot deployed in a middle school setting, revealing how
  students used and sometimes misused the system. Building on these findings, we
  propose a new taxonomy of content-based risks for minors and introduce
  MinorBench, an open-source benchmark designed to evaluate LLMs on their ability
  to refuse unsafe or inappropriate queries from children. We evaluate six
  prominent LLMs under different system prompts, demonstrating substantial
  variability in their child-safety compliance. Our results inform practical
  steps for more robust, child-focused safety mechanisms and underscore the
  urgency of tailoring AI systems to safeguard young users.
paper_ai_summary: The study identifies content-based risks for minors using LLM-based chatbots in schools, proposes a taxonomy for these risks, and introduces MinorBench to evaluate LLM safety for children.
paper_ai_keywords: Large Language Models LLMs chatbot content-based risks minors MinorBench child-safety compliance