Enhancing transparency understanding using machine learning and visual analytics

In today’s digital age, users are frequently confronted with lengthy terms and conditions documents associated with various products and services. Such documents often reference multiple entities (such as stakeholders, individuals, and users), with certain entities repeated throughout, underscoring their relative importance within the text. This study proposes a novel approach to facilitate the comprehension of terms and conditions by enhancing the detection and weighting of entities, as well as identifying relationships among them. By leveraging machine learning techniques (particularly natural language processing (NLP)) in conjunction with visual analytics, we aim to improve transparency and accessibility. Furthermore, we present an improved version of TranspVis, a visual analytics system to provide a more intuitive representation of transparency-related information. The proposed approach is evaluated through a combination of case studies and user experiments, offering a comprehensive assessment of its utility in rendering complex legal documents more interpretable. The findings underscore the potential of such tools to support large-scale applications in legal domains, with expert feedback affirming the value and relevance of the proposed solution.