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.
Abstract (translated)
开放域视频平台提供丰富、个性化的内容，可支持健康、护理和教育应用，但其以参与度优化为核心的推荐算法可能使脆弱用户接触到不适宜或有害内容。此类风险在儿童导向和护理场景（如痴呆症护理）中尤为突出，因为内容在展示前必须满足个性化的安全约束。我们提出了SafeScreen（安全屏幕），这是一个以安全为先决条件的视频筛查框架，可在执行个性化安全约束的同时检索并呈现视频内容。SafeScreen并非依据相关性或热度对视频排序，而是将安全视为先决条件，通过自动化流水线对候选视频进行顺序审批或拒绝。该框架整合三大核心组件：(i) 基于用户画像提取个性化安全标准；(ii) 通过自适应问题生成与多模态视频检索增强分析（VideoRAG）进行证据驱动的评估；(iii) 基于大语言模型（LLM）的决策机制，在内容展示前验证其安全性、适宜性与相关性。此设计实现了对未策展视频库的可解释、实时筛查，且无需依赖预计算的安全标签。我们在痴呆症护理怀旧疗法案例研究中评估了SafeScreen，使用30个模拟患者档案和90个测试查询。结果表明，SafeScreen优先保障安全而非参与度，在80%-93%的案例中与YouTube的参与度优化排序产生分歧，同时在高安全覆盖率、合理性与事实 groundedness 方面保持优异表现——这一结论已通过LLM评估与领域专家验证。
URL
https://arxiv.org/abs/2604.03264
