Explainable AI: XAI-Guided Context-Aware Data Augmentation

• URL: http://arxiv.org/abs/2506.03484v1
• Date: Wed, 04 Jun 2025 01:47:24 GMT
• Title: Explainable AI: XAI-Guided Context-Aware Data Augmentation
• Authors: Melkamu Abay Mersha, Mesay Gemeda Yigezu, Atnafu Lambebo Tonja, Hassan Shakil, Samer Iskander, Olga Kolesnikova, Jugal Kalita,
• Abstract summary: The scarcity of labeled data remains a fundamental challenge in developing robust and generalizable AI models.<n>Conventional data augmentation techniques introduce noise, cause semantic drift, disrupt contextual coherence, lack control, and lead to overfitting.<n>We propose XAI-Guided Context-Aware Data Augmentation.<n>Our approach integrates an iterative feedback loop, which refines augmented data over multiple augmentation cycles based on explainability-driven insights.
• Score: 5.721631408241837
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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 generalizable 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-Aware 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.

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