Abstract:

In an era where digital privacy is of paramount concern, the complexity and variability of privacy policies pose a significant challenge for users and regulators alike. Paradoxically, even though the emergence of the digital realm has given rise to privacy concerns, the field of computer science has yet to address these challenges, transforming them into ethical dilemmas awaiting resolution within the discipline. This paper proposes a novel, AI-driven framework for automated analysis and grading of website privacy policies, drawing inspiration from the simplicity and effectiveness of nutrition labels on food products. Our approach employs Natural Language Processing (NLP) and machine learning algorithms to dissect and interpret the intricate language of privacy policies, moving towards a clear, unbiased assessment that enhances understanding and transparency.

We are developing a comprehensive tagging system that categorizes policy content into key areas such as Children's Privacy, Data Usage, Data Collection, and Data Sharing, with sub-tags highlighting specifics like AI-driven data collection methods. This system aims to break down complex privacy terms into manageable, easily understandable categories, similar to how nutrition labels simplify food content information.

A central innovation of our proposed framework is the introduction of a user-centric dashboard conceptualized on the model of food nutrition labels. This dashboard will present the analysis results intuitively and visually engagingly, enabling users to grasp the essence of a website's privacy practices. It aims to grade policies based on predefined criteria such as compliance with global standards (GDPR, CCPA), transparency, and user rights protection, thus providing an informative tool for end-users to make informed decisions regarding their online privacy.

The paper will discuss the collection of privacy policies, processing them through a NLP model, and subsequently applying a grading algorithm. The development and training of our machine learning models on an extensively annotated dataset of privacy policies are explored in detail, showcasing the robustness and accuracy of our analysis.

In addition to the technological aspects, we address the challenges of scalability, data security, and ethical considerations inherent in deploying such a system. Our solution ensures adaptability and continuous improvement in alignment with the evolving privacy landscape by providing a user-friendly interface and incorporating feedback mechanisms.

The paper highlights the potential of this automated grading system in fostering a more privacy-conscious online environment. By demystifying privacy policies and making them more accessible, we empower users with the knowledge to protect their personal data. The user-centric dashboard, in particular, marks a significant step forward in bridging the gap between complex legal language and the general public's understanding.

In conclusion, this paper contributes a novel, practical tool for evaluating privacy policies. By integrating advanced AI with a user-focused approach, we offer a solution that enhances transparency and accountability in privacy practices and promotes a higher standard of digital literacy. The implications of this research extend beyond academic discourse, presenting real-world applications significantly impacting how privacy policies are perceived in our digital society.

Keywords:

Privacy, learning, policy, user dashboard, NLP, AI.