Explainable AI: XAI-Guided Context-Aware Data Augmentation

Explainable AI: XAI-Guided Context-A ware Data Augmentation Melkamu Abay Mersha a,, Mesay Gemeda Yigezu b, Atnafu Lambebo Tonja c, Hassan Shakil a, Samer Iskander a, Olga Kolesnikova b, Jugal Kalita a a College of Engineering and Applied Science, University of Colorado Colorado Springs, Colorado Springs, 80918, CO, USA b Instituto Polit ecnico Nacional (IPN), Centro de Investigaci on en Computaci on (CIC), 07738, Mexico City, Mexico c Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dhabi, UAEAbstract Explainable AI (XAI) has emerged as a powerful tool for improving the performance of AI models, going beyond providing model transparency and interpretability. The scarcity of labeled data remains a fundamental challenge in developing robust and gener-alizable AI models, particularly for low-resource languages. Conventional data augmentation techniques introduce noise, cause semantic drift, disrupt contextual coherence, lack control, and lead to overfitting. To address these challenges, we propose XAI-Guided Context-A ware Data Augmentation. This novel framework leverages XAI techniques to modify less critical features while selectively preserving most task-relevant features. Our approach integrates an iterative feedback loop, which refines augmented data over multiple augmentation cycles based on explainability-driven insights and the model performance gain. Our experimental results demonstrate that XAI-SR-BT and XAI-PR-BT improve the accuracy of models on hate speech and sentiment analysis tasks by 6.6% and 8.1%, respectively, compared to the baseline, using the Amharic dataset with the XLM-R model. XAI-SR-BT and XAI-PR-BT outperform existing augmentation techniques by 4.8% and 5%, respectively, on the same dataset and model. Overall, XAI-SR-BT and XAI-PR-BT consistently outperform both baseline and conventional augmentation techniques across all tasks and models. This study provides a more controlled, interpretable, and context-aware solution to data augmentation, addressing critical limitations of existing augmentation techniques and offering a new paradigm shift for leveraging XAI techniques to enhance AI model training. Introduction The rapid advancement of large language models (LLMs), such as GPT [1] and BERT [2], has transformed various domains, including safety-critical applications. Despite their impressive capabilities, these models operate as black boxes, raising concerns about transparency, trustworthiness, and in-terpretability. Explainable Artificial Intelligence (XAI) has emerged as a key solution to these concerns, offering insights into the decision-making processes of AI models.