开发用户可控的AI助手，让社交平台更安全、有自主权且护心。
Towards a Humanized Social-Media Ecosystem: AI-Augmented HCI Design Patterns for Safety, Agency & Well-Being
- 在浏览器中部署用户自有的可解释AI中间层，实时干预平台算法
- 五种设计模式实测有效：重写帖子、检测信息真伪、精准调优内容流等
- 适合关注数字健康、隐私控制与人机协同设计的研究者和开发者
社交平台连接数十亿人，但以参与度为核心的算法常将用户视为对象而非伙伴，加剧压力、误导信息传播并削弱掌控感。本文提出人类层人工智能（HL-AI）——一种用户拥有的、可解释的浏览器级中间件，位于平台逻辑与界面之间。该系统使用户在不依赖平台配合的情况下，实现即时、切实的控制权。我们构建了支持Chrome/Edge的原型，实现五类代表性设计模式：上下文感知的帖子重写器、帖子真实性评分器、细粒度内容流调节器、微退避代理与恢复模式，并提供统一数学框架，平衡用户效用、自主成本与风险阈值。评估涵盖技术精度、可用性及行为结果。实验表明，这些工具能帮助用户在伤害发生前重写内容、以真实性提示阅读、有意识地调整信息流、中断强迫性循环，并在遭受骚扰时寻求庇护，同时通过可解释性与可覆盖选项保障自主权。该原型为现有内容流注入安全、自主与福祉提供了可行路径，呼吁开展跨文化用户验证。
原文摘要 · Abstract (English)
Social platforms connect billions of people, yet their engagement-first algorithms often work on users rather than with them, amplifying stress, misinformation, and a loss of control. We propose Human-Layer AI (HL-AI)--user-owned, explainable intermediaries that sit in the browser between platform logic and the interface. HL-AI gives people practical, moment-to-moment control without requiring platform cooperation. We contribute a working Chrome/Edge prototype implementing five representative pattern frameworks--Context-Aware Post Rewriter, Post Integrity Meter, Granular Feed Curator, Micro-Withdrawal Agent, and Recovery Mode--alongside a unifying mathematical formulation balancing user utility, autonomy costs, and risk thresholds. Evaluation spans technical accuracy, usability, and behavioral outcomes. The result is a suite of humane controls that help users rewrite before harm, read with integrity cues, tune feeds with intention, pause compulsive loops, and seek shelter during harassment, all while preserving agency through explanations and override options. This prototype offers a practical path to retrofit today's feeds with safety, agency, and well-being, inviting rigorous cross-cultural user evaluation.
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