How Well do LLMs Assist Parents in Assessing Child Appropriateness of Videos?

Key findings from this study

This research indicates that:

• LLMs prove less effective at independently determining video appropriateness for children under 7 than direct parental assessment.
• LLM-generated video descriptions substantially aid parental decision-making despite inaccurate initial appropriateness classifications.
• Explainability in model outputs addresses transparency deficits that undermine trust in current automated content assessment systems.

Overview

Large language models (LLMs) present a potential solution to scale assessment of video appropriateness for young children in digital environments. Manual curation remains impractical given the volume of uploaded content. Current automated systems inadequately account for the diversity in parental preferences, cultural values, and supervision approaches. Existing methods also lack transparency and explainability necessary for parental trust. This research evaluates LLM capability to assess video appropriateness for children under 7 while providing explainable rationales aligned with parental values.

Methods and approach

The study examined how LLMs perform in assessing video appropriateness for young children. Researchers compared LLM performance against parental judgments and evaluated the explanations models generated. The analysis assessed both the accuracy of appropriateness determinations and the utility of model-generated descriptions in supporting parental decision-making.

Results

LLMs demonstrated limited direct effectiveness in determining video appropriateness independently. However, the models generated valuable descriptive summaries of video content that parents found useful. The descriptions provided sufficient detail to aid parental judgment, even when the models' initial appropriateness classifications diverged from parental preferences. This suggests LLMs function more effectively as information synthesis tools than as standalone decision-makers in this domain.

Implications

LLM-based systems warrant consideration as supporting tools within parental decision frameworks rather than autonomous arbiters of content appropriateness. The explainability dimension of LLM outputs addresses a critical gap in current automated approaches, potentially building parental confidence through transparency. Systems designed around description and explanation rather than categorical determinations may better accommodate the inherent diversity in parental supervision philosophies and cultural contexts.

Future work should investigate how to optimize LLM outputs for different demographic groups and parental approaches. Integration of parental preference elicitation could enhance system utility. The research suggests hybrid systems combining LLM descriptions with parental input mechanisms may outperform purely automated solutions.

Scope and limitations

This summary is based on the study abstract and available metadata. It does not include a full analysis of the complete paper, supplementary materials, or underlying datasets unless explicitly stated. Findings should be interpreted in the context of the original publication.