Terms of Service Fairness Analyser using Machine Learning

Abstract

Abstract: Terms of Service (ToS) are fundamental factors in the creation of physical as well as online legally relevant relationships. Very often, they disregard the consumer protection law. In this perspective, a relevant issue is that public agencies in charge of control concretely lack the resources needed to effectively fight against such unlawful practices. We propose a definition of ToS unfairness and a novel machine learning-based approach to classify clauses in ToS, represented by using sentence embedding, into both categories and fairness classes. We use Naïve Bayes Classifier to achieve the desired categorization and result. Terms of Service (ToS) are fundamental factors in the creation of physical as well as online legally relevant relationships. They not only define mutual rights and obligations but also inform users about contract key issues that, in online settings, span from liability limitations to data management and processing conditions. Despite their crucial role, however, ToS are often neglected by users that frequently accept without even reading what they agree upon, representing a critical issue when there exist potentially unfair clauses. To enhance users’ awareness and uphold legal safeguards, we first propose a definition of ToS unfairness based on a novel unfairness measure computed counting the unfair clauses contained in a ToS, and therefore, weighted according to their direct impact on the customers concrete interests.