Visual Analytics Using Machine Learning for Transparency Requirements

Samiha Fadloun (), Khadidja Bennamane, Souham Meshoul (), Mahmood Hosseini and Kheireddine Choutri
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Samiha Fadloun: Ecole Nationale Supérieure d’Informatique (ESI), BP 68M, Oued Smar, Algiers 16309, Algeria
Khadidja Bennamane: Ecole Nationale Supérieure d’Informatique (ESI), BP 68M, Oued Smar, Algiers 16309, Algeria
Souham Meshoul: Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
Mahmood Hosseini: JPMorgan Chase, 1 Chaseside, Bournemouth BH7 7DA, UK
Kheireddine Choutri: Aeronautical Sciences Laboratory, Aeronautical and Spatial Studies Institute, Blida 1 University, Blida 0900, Algeria

Mathematics, 2023, vol. 11, issue 14, 1-18

Abstract: Problem solving applications require users to exercise caution in their data usage practices. Prior to installing these applications, users are encouraged to read and comprehend the terms of service, which address important aspects such as data privacy, processes, and policies (referred to as information elements). However, these terms are often lengthy and complex, making it challenging for users to fully grasp their content. Additionally, existing transparency analytics tools typically rely on the manual extraction of information elements, resulting in a time-consuming process. To address these challenges, this paper proposes a novel approach that combines information visualization and machine learning analyses to automate the retrieval of information elements. The methodology involves the creation and labeling of a dataset derived from multiple software terms of use. Machine learning models, including naïve Bayes, BART, and LSTM, are utilized for the classification of information elements and text summarization. Furthermore, the proposed approach is integrated into our existing visualization tool TranspVis to enable the automatic detection and display of software information elements. The system is thoroughly evaluated using a database-connected tool, incorporating various metrics and expert opinions. The results of our study demonstrate the promising potential of our approach, serving as an initial step in this field. Our solution not only addresses the challenge of extracting information elements from complex terms of service but also provides a foundation for future research in this area.

Keywords: information visualization; visual analytics; transparency analytics; machine learning; natural language processing (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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