Adoption of Explainable Natural Language Processing: Perspectives from Industry and Academia on Practices and Challenges
This study addresses the underexplored practical adoption and effectiveness of explainable NLP for industry and academic practitioners, identifying key challenges to improve deployment in real-world applications.
The paper investigated practitioners' experiences with explainable NLP methods through qualitative interviews, revealing conceptual gaps, low satisfaction, and evaluation challenges, and emphasized the need for clear definitions and user-centric frameworks for better adoption.
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.