An Automated Approach to Simplifying Privacy Policies for Empowered User Understanding

What is it about?

Service providers require users to read Terms of Service (ToS) documents before accessing services, but their legal language and length are barriers preventing users from reading through the content. This typically leads consumers to unknowingly agree to terms which might entail privacy concerns and undesirable information use. Failure to read policy documents also results in data leakage and data breach concerns. Further, when users do read them, policy documents are often difficult to understand due to their length and complex legal terminology. To deal with these problems, we apply multiple Machine Learning (ML) methods to automatically annotate and summarize contents of ToS and related documents. This research analyzes the competitive performance of popular simple machine learning models versus more complex models like BERT and its variations on classifying text into different categories in these documents. We also compare the performance of the GPT-4 Turbo LLM with traditional models. Because of our ongoing commitment to improving legal accessibility, we plan to integrate eXplainable Artificial Intelligence (XAI) into our proposed system to enhance transparency, reliability, and overall user experience. To gauge the effectiveness of our solution, we plan to conduct two user studies to collect feedback on the tool’s clarity, ease of use, and instructional value. If successful, our research endeavors will culminate in a scalable system that simplifies complex legal documents, empowering users to make informed decisions.

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Why is it important?

• Increased Awareness of Data Rights: Users will become more knowledgeable about their rights regarding data privacy. This includes understanding what data is being collected, how their data is being used, and what control they have. Companies might simplify their privacy policies to make them more user-friendly. Clear, concise, and transparent policies could become a competitive advantage. • Empowered Decision-Making: With a clear understanding of privacy terms, users can make more informed decisions about which services to use and what terms they are comfortable agreeing to allow. This empowerment could lead to more selective consent, where users opt in or out based on their privacy preferences. • Demand for Better Privacy Practices: As public knowledge grows, there could be increased pressure on institutions (i.e., businesses or governments) to adopt or require more user-friendly privacy practices. This could lead to more transparent data handling and potentially even influence legislative changes. Companies might seek to collaborate more with privacy experts, legal advisors, and technology providers to improve their privacy policies and practices. • Behavioral Changes in Digital Spaces: With a better grasp of privacy implications, users might alter their behavior online, such as being more cautious about sharing personal information or using privacy-enhancing tools and settings. The awareness of such an analysis in the think tank of the company can drive innovation, leading companies to develop new technologies and methods for protecting user privacy. Increased awareness can also ensure better compliance with existing and emerging data protection regulations, thereby avoiding legal repercussions. • Trust in Digital Services: By demonstrating a commitment to transparency and user privacy, companies can build greater trust with their customers, which is crucial for brand reputation and customer loyalty. If companies respond positively by simplifying their privacy policies and respecting user preferences, this could lead to increased trust in these services. Companies might reevaluate their data collection and processing practices, leading to more ethical and responsible use of user data. Conversely, if users become aware of overly invasive or unfair practices, it could lead to a backlash against certain companies, prompting them to revise their policies.

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