面向社交媒体中可解释AI推荐的上下文感知可视化：用户对齐解释的愿景
Context-Aware Visualization for Explainable AI Recommendations in Social Media: A Vision for User-Aligned Explanations
AI总结：
针对社交媒体AI推荐解释通用、未对齐用户需求的问题，提出首个在单一流水线中同时适配解释风格和粒度的上下文感知可视化解释系统，将通过30名X用户的公开试点验证其对决策和信任的影响。
摘要：
如今的社交媒体平台力求通过AI推荐提升用户体验，但由于用户不理解推荐背后的原因，这类推荐的价值大打折扣。这一问题的根源在于社交媒体中的可解释性是通用化的，缺乏与用户特定需求的对齐。在这篇愿景论文中，我们提出了一个具备多种解释方法的可视化解释系统，勾勒出一个用户分层且上下文感知的解释层。所提出的系统以用户需求和场景的多样性为框架，以不同的可视化形式呈现解释：包括为AI专家提供的技术细节版本，以及为普通用户提供的简化版本。我们的框架是首个在单一流水线中同时适配解释风格（可视化vs.数值化）和粒度（专家级vs.普通级）的框架。一项包含30名X用户的公开试点将验证其对决策和信任的影响。
英文摘要：
Social media platforms today strive to improve user experience through AI recommendations, yet the value of such recommendations vanishes as users do not understand the reasons behind them. This issue arises because explainability in social media is general and lacks alignment with user-specific needs. In this vision paper, we outline a user-segmented and context-aware explanation layer by proposing a visual explanation system with diverse explanation methods. The proposed system is framed by the variety of user needs and contexts, showing explanations in different visualized forms, including a technically detailed version for AI experts and a simplified one for lay users. Our framework is the first to jointly adapt explanation style (visual vs. numeric) and granularity (expert vs. lay) inside a single pipeline. A public pilot with 30 X users will validate its impact on decision-making and trust.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
