用户更愿接受有解释的社交推荐，尤其涉及不熟内容时
To Explain Or Not To Explain: An Empirical Investigation Of AI-Based Recommendations On Social Media Platforms
- 通过用户访谈研究推荐系统解释需求
- 解释能提升透明度、信任感与可理解性
- 用户需要简洁非技术解释和可控信息流
基于AI的社交媒体推荐具有提升用户体验的巨大潜力，但常因不符合用户兴趣而引发负面体验。同时，推荐系统作为黑箱，带来可理解性和透明度问题。本文从终端用户视角出发，以主流平台Facebook为研究对象，招募日常用户进行定性分析，探讨其对内容推荐、可理解性及可解释性的看法。结果表明，用户在遇到不熟悉内容时普遍需要解释，且关注在线数据安全。他们期望获得简洁、非技术化的解释，并具备可控的信息流动机制。此外，解释显著影响用户对透明度、信任度与可理解性的感知。最后，研究提炼出设计启示，并构建了基于数据分析的综合框架。
原文摘要 · Abstract (English)
AI based social media recommendations have great potential to improve the user experience. However, often these recommendations do not match the user interest and create an unpleasant experience for the users. Moreover, the recommendation system being a black box creates comprehensibility and transparency issues. This paper investigates social media recommendations from an end user perspective. For the investigation, we used the popular social media platform Facebook and recruited regular users to conduct a qualitative analysis. We asked participants about the social media content suggestions, their comprehensibility, and explainability. Our analysis shows users mostly require explanation whenever they encounter unfamiliar content and to ensure their online data security. Furthermore, the users require concise, non-technical explanations along with the facility of controlled information flow. In addition, we observed that explanations impact the users perception of transparency, trust, and understandability. Finally, we have outlined some design implications and presented a synthesized framework based on our data analysis.
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