Explainability in Practice: A Survey of Explainable NLP Across Various Domains

TL;DR

This survey reviews the practical deployment of explainable NLP across various domains, highlighting challenges, applications, and future directions to improve transparency and trust in AI models.

Contribution

It provides a comprehensive overview of domain-specific explainable NLP applications, addressing real-world challenges and proposing future research avenues.

Findings

Explainability enhances trust in NLP models across sectors.

Domain-specific challenges influence explainability methods.

Future research should focus on real-world applicability and human-AI interaction.

Abstract

Natural Language Processing (NLP) has become a cornerstone in many critical sectors, including healthcare, finance, and customer relationship management. This is especially true with the development and use of advanced models such as GPT-based architectures and BERT, which are widely used in decision-making processes. However, the black-box nature of these advanced NLP models has created an urgent need for transparency and explainability. This review explores explainable NLP (XNLP) with a focus on its practical deployment and real-world applications, examining its implementation and the challenges faced in domain-specific contexts. The paper underscores the importance of explainability in NLP and provides a comprehensive perspective on how XNLP can be designed to meet the unique demands of various sectors, from healthcare's need for clear insights to finance's emphasis on fraud detection and risk assessment. Additionally, this review aims to bridge the knowledge gap in XNLP literature by offering a domain-specific exploration and discussing underrepresented areas such as real-world applicability, metric evaluation, and the role of human interaction in model assessment. The paper concludes by suggesting future research directions that could enhance the understanding and broader application of XNLP.