New Holistic AI Explainability Framework Addresses "Black Box" Challenges Across Entire Workflow

A groundbreaking paper introduces Holistic Explainable Artificial Intelligence (HXAI), a novel framework designed to embed transparency and interpretability into every stage of the machine learning workflow. The work, titled "A Comprehensive Perspective on Explainable AI across the Machine Learning Workflow," aims to overcome the traditional "black box" nature of AI models by extending explainability beyond mere output interpretation.

Authored by Rohan Paul, an Associate Professor at IIT Delhi specializing in AI and robotics, and his collaborators, the paper details how HXAI unifies six critical components of the data analysis pipeline: data, analysis set-up, learning process, model output, model quality, and the communication channel. This comprehensive approach ensures that insights can be trusted from data inception to final presentation.

"This paper shows how to make AI explain itself across the whole pipeline, start to finish," stated Rohan Paul in a recent social media post. He further elaborated that the work "extends explainability to cover data choices, training decisions, checks on quality, and how results are presented."

The HXAI framework is user-centric, tailoring explanations to three distinct audiences: domain experts, data analysts, and data scientists. This adaptability allows the system to communicate in terms of business outcomes, quick diagnostics, or detailed developer traces, depending on the user's needs.

A significant contribution of the research is a 112-item question bank, used to benchmark existing tools and identify critical gaps in current explainability solutions. The study found strong support for model outputs but noted weak support for explanations related to data decisions and reliability checks.

The paper also sketches the concept of an LLM agent that can integrate various explanation methods and rephrase them for specific roles, ensuring explanations are "accurate, actionable, and easy to use," rather than just providing more charts. This advancement promises to bridge the communication gap between AI developers and domain experts, fostering greater trust and broader adoption of AI technologies in critical applications.