• ai
• artificial intelligence
• bert
• deep learning
• explainability
• interpretability
• legal dataset
• long text
• sidu
• text classification
• transformers
• xai

Explainable Artificial Intelligence (XAI) plays a crucial role in making deep learning models more interpretable, particularly in high-stakes domains such as the legal field. This thesis explores the development of a novel XAI method inspired by the SIDU method for long-text classification in legal and sentiment analysis datasets. Specifically, it examines how different classification techniques—such as First-512 tokens, Last-512 tokens, Random-512 tokens, and Random-512 with rationale—affect model interpretability. The study focuses on two transformer-based architectures, BERT and RoBERTa, and introduces novel XAI approaches, including Cosine Similarity Masking (thresholded and ranged) and Persistent Homology Masking (based on angular and Euclidean distances), alongside SHAP as a baseline comparison. A key aspect of this work is the utilization of datasets with annotated rationales, which highlight the most relevant text segments for classification. Both qualitative and quantitative analyses demonstrate that the proposed XAI methods produce explanations that align with human-annotated rationales, indicating that the models' internal representations capture meaningful semantic information.