揭露社交应用中本地模型的隐形运作，揭示用户认知与行为变化。
"Impressively Scary:" Exploring User Perceptions and Reactions to Unraveling Machine Learning Models in Social Media Applications
- 通过用户研究发现，90%以上用户不清楚模型何时何地运行。
- 8名参与者在了解模型后出现长期使用习惯改变。
- 适合关注隐私透明与AI可解释性的研究人员和产品设计者。
部署于社交应用中的机器学习模型（如实时人脸滤镜）会读取用户面部数据并暴露敏感属性，但其使用时机、地点及方式对用户而言高度不透明。本文通过21名用户的实验研究发现，受访者普遍不了解Instagram与TikTok中模型的具体输出内容及触发条件。在被告知模型功能后，8名参与者表现出长期行为改变。研究揭示了面向本地用户数据的机器学习模型实现透明化的挑战与机遇。
原文摘要 · Abstract (English)
Machine learning models deployed locally on social media applications are used for features, such as face filters which read faces in-real time, and they expose sensitive attributes to the apps. However, the deployment of machine learning models, e.g., when, where, and how they are used, in social media applications is opaque to users. We aim to address this inconsistency and investigate how social media user perceptions and behaviors change once exposed to these models. We conducted user studies (N=21) and found that participants were unaware to both what the models output and when the models were used in Instagram and TikTok, two major social media platforms. In response to being exposed to the models' functionality, we observed long term behavior changes in 8 participants. Our analysis uncovers the challenges and opportunities in providing transparency for machine learning models that interact with local user data.
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