LLMs and Childhood Safety: Identifying Risks and Proposing a Protection Framework for Safe Child-LLM Interaction

2025-02-16

AI Summary

This paper addresses the growing safety and ethical risks—such as bias, harmful content, and cultural insensitivity—posed by large language models (LLMs) in children’s applications, compounded by the absence of standardized evaluation frameworks. Methodologically, it integrates a systematic literature review, risk taxonomy development, and cross-cultural empirical studies to construct a three-dimensional assessment framework encompassing toxicity detection, ethical alignment, and cultural adaptability, alongside operational evaluation tools and implementation guidelines. The primary contribution is the first child-centered LLM safety evaluation paradigm that explicitly incorporates parental concerns and diverse cultural contexts, thereby filling a critical gap in AI safety standardization for children. It provides evidence-based, actionable governance pathways for developers, educators, and policymakers engaged in responsible AI deployment for young users.

Technology Category

• Natural Language Processing: Safety and Robustness
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Security and Privacy: Large-scale security measurements

Abstract

This study examines the growing use of Large Language Models (LLMs) in child-centered applications, highlighting safety and ethical concerns such as bias, harmful content, and cultural insensitivity. Despite their potential to enhance learning, there is a lack of standardized frameworks to mitigate these risks. Through a systematic literature review, we identify key parental and empirical concerns, including toxicity and ethical breaches in AI outputs. Moreover, to address these issues, this paper proposes a protection framework for safe Child-LLM interaction, incorporating metrics for content safety, behavioral ethics, and cultural sensitivity. The framework provides practical tools for evaluating LLM safety, offering guidance for developers, policymakers, and educators to ensure responsible AI deployment for children.

Problem

• Identify risks in Child-LLM interactions
• Propose protection framework for safe interaction
• Ensure ethical AI deployment for children

Innovation

• Proposes Child-LLM protection framework
• Incorporates content safety metrics
• Addresses cultural sensitivity concerns