Analysing User Reviews to Identify User Concerns Around Permissions in AI Apps

AI 中文解读

AI自动“读心”用户评论，帮你识别App的隐私隐患。这项研究用机器学习分析AI应用的评论区，能自动把用户关于权限的吐槽分类，准确率高达82%。以前要靠人工一条条标记数据，成本高又慢，现在让AI自己生成样本来训练模型，省时省力。有趣的是，研究发现用户更习惯按对App的好感度来表达担忧，而不是按权限类型。这意味着你要是对某个App印象不好，连它要定位权限都会觉得可疑。对普通人来说，以后下载App前，AI能帮你自动汇总“这App有没有乱要权限”的口碑，避免被隐私条款绕晕；对开发者而言，也能更快从差评中找到产品漏洞，及时修复信任危机。

Artificial intelligence is increasingly embedded in everyday software, making its integration into mobile apps inevitable. However, AI mobile app developers are not always versed in security and privacy best practices, leaving users to monitor their own security and understand how apps use their data. App reviews capture real user experiences, helping others make informed decisions before downloading. This paper presents a machine learning model for classifying AI app reviews into permission-related categories. Because user reviews are unstructured, assembling a conventional labeled training set is difficult. To address this, AI-generated security and permission reviews are used to identify relevant training examples from a large corpus of human-written reviews, eliminating the need for manual annotation. The proposed approach classified permission reviews with an accuracy of 82%. Analysis shows that users organise their concerns by sentiment toward the requesting app rather than specific permission types, with implications for users, developers, and platform administrators.