A Comprehensive Perspective on Explainable AI across the Machine Learning Workflow — 2025-08-18
Summary
The article proposes a new framework called Holistic Explainable AI (HXAI) that aims to enhance the transparency and interpretability of AI models across the entire machine learning workflow. Unlike traditional methods that primarily focus on model outputs, HXAI integrates explainability into various stages, including data analysis, learning processes, and model quality, all while considering the diverse needs of different user groups.
Why This Matters
This approach is crucial as AI systems become increasingly complex and their decisions impact high-stakes fields such as healthcare and finance. By providing a structured framework for explainability, HXAI helps build trust and understanding among users, including non-experts, thereby facilitating broader acceptance of AI technologies.
How You Can Use This Info
Working professionals can leverage the HXAI framework to improve their AI projects by ensuring that explainability is embedded at every step of the machine learning process. Utilizing the comprehensive question bank and taxonomy provided in the article can guide the development of more interpretable and trustworthy AI systems, ultimately enhancing collaboration between technical and non-technical stakeholders. For more insights, you can explore tools and methodologies outlined in the article.