SafeScreen: A Safety-First Screening Framework for Personalized Video Retrieval for Vulnerable Users

arXiv cs.CV / 4/7/2026

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Key Points

The article proposes SafeScreen, a safety-first framework for personalized open-domain video retrieval that prevents vulnerable users from being exposed to inappropriate or harmful content in care and child-directed settings.
SafeScreen enforces individualized safety constraints as a prerequisite by screening candidate videos through a sequential approval/rejection pipeline rather than using engagement-optimized ranking.
The system extracts safety criteria from user profiles, performs evidence-grounded assessments using adaptive question generation plus multimodal VideoRAG analysis, and then uses LLM-based decision-making to verify safety, appropriateness, and relevance.
Evaluation in a dementia-care reminiscence case study with synthetic profiles shows SafeScreen prioritizes safety over engagement, diverging from YouTube-like ranking in the majority of cases while maintaining strong safety coverage and quality metrics.

Abstract

Open-domain video platforms offer rich, personalized content that could support health, caregiving, and educational applications, but their engagement-optimized recommendation algorithms can expose vulnerable users to inappropriate or harmful material. These risks are especially acute in child-directed and care settings (e.g., dementia care), where content must satisfy individualized safety constraints before being shown. We introduce SafeScreen, a safety-first video screening framework that retrieves and presents personalized video while enforcing individualized safety constraints. Rather than ranking videos by relevance or popularity, SafeScreen treats safety as a prerequisite and performs sequential approval or rejection of candidate videos through an automated pipeline. SafeScreen integrates three key components: (i) profile-driven extraction of individualized safety criteria, (ii) evidence-grounded assessments via adaptive question generation and multimodal VideoRAG analysis, and (iii) LLM-based decision-making that verifies safety, appropriateness, and relevance before content exposure. This design enables explainable, real-time screening of uncurated video repositories without relying on precomputed safety labels. We evaluate SafeScreen in a dementia-care reminiscence case study using 30 synthetic patient profiles and 90 test queries. Results demonstrate that SafeScreen prioritizes safety over engagement, diverging from YouTube's engagement-optimized rankings in 80-93% of cases, while maintaining high levels of safety coverage, sensibleness, and groundedness, as validated by both LLM-based evaluation and domain experts.