PEAK: Explainable Privacy Assistant through Automated Knowledge Extraction
This work addresses the need for explainable AI in online privacy management, though it is incremental as it builds on existing privacy assistant frameworks.
The paper tackles the problem of making privacy assistants more explainable by developing a system that generates automated explanations for privacy decisions, with a user study showing that users find these explanations useful and easy to understand.
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.