Adoption of Explainable Natural Language Processing: Perspectives from Industry and Academia on Practices and Challenges

TL;DR

This paper explores how industry and academia adopt and perceive explainable NLP, highlighting challenges, gaps, and the need for clearer frameworks to improve practical implementation.

Contribution

It provides a comparative analysis of practitioners' experiences with explainability methods, revealing gaps between research and practice in explainable NLP.

Findings

Low satisfaction with current explainability methods

Identification of conceptual gaps in explainability

Highlighting evaluation challenges in real-world applications

Abstract

The field of explainable natural language processing (NLP) has grown rapidly in recent years. The growing opacity of complex models calls for transparency and explanations of their decisions, which is crucial to understand their reasoning and facilitate deployment, especially in high-stakes environments. Despite increasing attention given to explainable NLP, practitioners' perspectives regarding its practical adoption and effectiveness remain underexplored. This paper addresses this research gap by investigating practitioners' experiences with explainability methods, specifically focusing on their motivations for adopting such methods, the techniques employed, satisfaction levels, and the practical challenges encountered in real-world NLP applications. Through a qualitative interview-based study with industry practitioners and complementary interviews with academic researchers, we systematically analyze and compare their perspectives. Our findings reveal conceptual gaps, low satisfaction with current explainability methods, and highlight evaluation challenges. Our findings emphasize the need for clear definitions and user-centric frameworks for better adoption of explainable NLP in practice.