在绘制关于个人的数据驱动推理时增强透明度和控制
Enhancing Transparency and Control when Drawing Data-Driven Inferences about Individuals
摘要：
最近的研究表明，在社交网站（如Facebook）上披露的信息可以用来预测个人特征，其准确性高得惊人。在本文中，我们研究了一种方法，让网上用户的透明度，为什么某些推论是关于他们的统计模型，并控制，以抑制这些推论隐藏（“掩盖”）某些个人信息的推断。我们使用这种方法来检查这种透明度和控制是否是一个合理的目标，通过评估用户实际上抑制推理的困难程度。将该方法应用于Facebook上大量真实用户的数据，我们发现用户必须只隐藏她的Facebook喜欢的一小部分，以抑制对他们个人特征的推断。然而，我们也表明，作为回应，公司可以改变其用户模型，使隐形更加困难。
英文摘要：
Recent studies have shown that information disclosed on social network sites (such as Facebook) can be used to predict personal characteristics with surprisingly high accuracy. In this paper we examine a method to give online users transparency into why certain inferences are made about them by statistical models, and control to inhibit those inferences by hiding ("cloaking") certain personal information from inference. We use this method to examine whether such transparency and control would be a reasonable goal by assessing how difficult it would be for users to actually inhibit inferences. Applying the method to data from a large collection of real users on Facebook, we show that a user must cloak only a small portion of her Facebook Likes in order to inhibit inferences about their personal characteristics. However, we also show that in response a firm could change its modeling of users to make cloaking more difficult.
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。
