EXPLAINABLE MACHINE LEARNING FOR TEXT CLASSIFICATION: A NOVEL APPROACH TO TRANSPARENCY AND INTERPRETABILITY

The increasing use of machine learning (ML) models for text classification has sparked debates about their interpretability and transparency. Despite their exceptional performance in applications like sentiment analysis, spam detection, and topic categorization, the often-obscure nature of ML models presents challenges for users seeking to understand the decision-making process. Explainable Machine Learning (XML) aims to tackle these issues by offering human-comprehensible explanations of model outputs, thereby building trust and enabling improved decision-making in critical sectors such as healthcare, legal, and finance. This study offers a thorough examination of XML techniques applied to text classification models, encompassing attention mechanisms, feature importance methods, and post-hoc interpretability frameworks like LIME and SHAP. Furthermore, we assess these techniques in terms of their capacity to enhance model transparency, weighing the trade-offs between interpretability and performance. The research also delves into the future trajectory of XML in text classification, including the incorporation of user-focused explanations and regulatory requirements for AI transparency. Our results indicate that explainability not only enhances model trustworthiness but also aids in identifying model biases and enhancing overall performance.