Interpretable Bangla Sarcasm Detection using BERT and Explainable AI

Language-specific AI systems require dedicated datasets and models: The paper demonstrates that sarcasm detection in Bangla language had not been adequately addressed despite significant progress in English. The researchers created a new Bangla sarcasm dataset (BanglaSarc) from social media, highlighting that responsible AI development requires investing in underrepresented languages rather than assuming English-trained models will transfer effectively.

Modern AI models dramatically outperform traditional approaches: The BERT-based system achieved 99.60% accuracy compared to 89.93% for traditional machine learning algorithms, showing that newer deep learning approaches can substantially improve performance on complex language tasks like detecting sarcasm, which has real implications for content moderation and threat detection systems.

Explainability is essential for deploying AI in real-world applications: The paper specifically incorporated LIME (Local Interpretable Model-Agnostic Explanations) to make the sarcasm detection system interpretable. This is crucial for responsible AI because it allows users and moderators to understand why the system flagged content as sarcastic, rather than treating it as a "black box" decision.

Sarcasm detection has practical consequences for social media governance: The paper notes that automated sarcasm detection supports important downstream tasks including "threat detection" and "sentiment analysis," meaning errors in this system could affect content moderation decisions, misinformation identification, and user safety—underscoring why accuracy and explainability matter for responsible deployment.