In the realm of online privacy, privacy assistants play a pivotal role in empowering users to manage their privacy effectively. Although recent studies have shown promising progress in tackling tasks such as privacy violation detection and personalized privacy recommendations, a crucial aspect for widespread user adoption is the capability of these systems to provide explanations for their decision-making processes. This paper presents a privacy assistant for generating explanations for privacy decisions. The privacy assistant focuses on discovering latent topics, identifying explanation categories, establishing explanation schemes, and generating automated explanations. The generated explanations can be used by users to understand the recommendations of the privacy assistant. Our user study of real-world privacy dataset of images shows that users find the generated explanations useful and easy to understand. Additionally, the generated explanations can be used by privacy assistants themselves to improve their decision-making. We show how this can be realized by incorporating the generated explanations into a state-of-the-art privacy assistant.

翻译：在在线隐私领域，隐私助手在帮助用户有效管理隐私方面发挥着关键作用。尽管近期研究在隐私违规检测和个性化隐私推荐等任务上取得了显著进展，但促使这些系统被广泛采用的一个关键因素，是其能够为其决策过程提供解释的能力。本文提出了一种用于生成隐私决策解释的隐私助手。该隐私助手专注于发现潜在主题、识别解释类别、建立解释方案以及生成自动化解释。生成的解释可用于用户理解隐私助手的建议。我们基于真实世界图像隐私数据集的用户研究表明，用户认为生成的解释有用且易于理解。此外，生成的解释也可被隐私助手自身用于改进其决策。我们展示了如何通过将生成的解释融入最先进的隐私助手来实现这一点。