MinorBench: A hand-built benchmark for content-based risks for children
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.
67d3dd208d3b0ca8d5ca7f69
Shaun Khoo
67d3dd208d3b0ca8d5ca7f6a
Gabriel Chua
claimed_verified
2025-03-14T08:55:54.401Z
655f4ff710e5c5fbef30fd97
https://cdn-avatars.huggingface.co/v1/production/uploads/655f4ff710e5c5fbef30fd97/bI_KQnFof50HRqYBWpyu2.jpeg
Gabriel C
user
gabrielchua
67d3dd208d3b0ca8d5ca7f6b
Rachel Shong
MinorBench: A hand-built benchmark for content-based risks for children
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.
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.
Large Language Models LLMs chatbot content-based risks minors MinorBench child-safety compliance