Explaining the Deep Natural Language Processing by Mining Textual Interpretable Features

Black-box AI models limit real-world deployment: Despite achieving high accuracy, widely-used deep learning models for language processing (like LSTM and BERT) are difficult for end-users to understand, which restricts their practical application in sensitive domains where explainability is crucial.

Explainability is now a fundamental requirement: The paper emphasizes that explainability has become essential for future AI systems, suggesting that accuracy alone is insufficient for responsible deployment of language-based AI tools.

The framework provides human-readable explanations: T-EBAnO generates explanations in plain language that domain experts can understand, offering both local explanations (why a specific decision was made) and global explanations (how the model behaves overall), making AI decision-making more transparent.

Interpretable features reveal model reasoning: Rather than leaving decisions unexplained, the framework extracts specific textual features that influenced the model's predictions, allowing stakeholders to verify whether the AI is using appropriate reasoning or potentially problematic patterns.

Specialized solutions are needed for language AI: The paper identifies that existing explainability approaches are insufficient for deep natural language models specifically, highlighting the need for tailored transparency tools rather than one-size-fits-all solutions.