Effective Machine Learning Approach for Identification of Unlawful Practices in Online Terms of Service

Terms of Service (ToS) agreements, which set down the legal parameters guiding the interaction between service providers and users, have become more and more important as a result of the widespread usage of online services. However, consumers may find it challenging to grasp their rights and duties due to the long and complex legal writing in these contracts. Furthermore, some terms and conditions of ToS agreements could be interpreted as unfair or illegal. In this research, a thorough machine learning method for spotting possibly illegal behaviour in online TOSs is presented. Our approach is a multi-step procedure. We started by curating a sizable collection of ToS papers from different online sites and annotating it with illegal provisions that were found by experts. After that, lets pre-processed the text data using natural language processing (NLP) techniques, such as stop word removal, tokenization, and lemmatization. In order to categorise phrases as legal or illegal, we investigated a number of machine learning techniques for the identification job, such as support vector machines (SVM), random forests, and neural networks. We used feature engineering methods like word embeddings and TF -IDF vectorization to improve the model's performance. In an effort to improve the clauses' contextual awareness, we also tried with more advanced models like BERT and GPT. The models demonstrated a high degree of accuracy in identifying illegal activities when assessed through the use of precision, recall, and F1-score measures. In addition, we created an intuitive user interface that lets users submit ToS papers and instantly get an analysis that flags any provisions that could be illegal. By offering users more transparency and comprehension of the agreements they sign, this technology seeks to empower users. The outcomes show how well our machine learning method works to spot illegal activity in online to-S agreements, which greatly benefits consumer protection and legal compliance. In order to increase the tool's use, future development will concentrate on growing the dataset, enhancing the model's interpretability, and adding multilingual support.