How ML Practitioners Perceive Explainability: An Interview Study of Practices and Challenges
Abstract
Because of limited guidance on how to handle explainability requirements, we conducted a qualitative interview study with 14 ML practitioners from 11 companies on the perception and implementation of explainability in AI-based systems. The study identifies four main categories of explainability definitions, reveals that standardized approaches for addressing end-user needs are lacking, and categorizes challenges into eight areas. The findings highlight the need for a structured requirements engineering approach for explainability.
Fields of Study & Subjects
Explainable Artificial Intelligence (XAI)
Ethics and Social Impacts of AI
Software Engineering Techniques and Practices
Publication details
- Journal / source
- VU Research Portal
- Publication date
- 1 January 2026
- Language
- EN
- Document type
- Book chapter