Abstract
Comment: 13 pages, 1 figure = this paper is a preprint of the workshop [Explainable AI in Space] paper for IJCAI ECAI 2026 conference
Challenges in Evaluating Explanation Methods for Static and Evolving Data
- Authors
- Jerzy Stefanowski
- Categories
- cs.AI
Brief
Jerzy Stefanowski's paper tackles evaluation gaps in XAI by demonstrating issues with the DetoxAI image-recognition system (bias detection, concept unlearning), presenting a human-grounded evaluation example for image-classification explanations, and exploring methods to adapt explanations—notably counterfactuals—to evolving data streams with concept drift. It emphasizes challenges in tracking co-evolution of data, models, and explanations and is a 13-page EASi 2026 workshop preprint.
Why it matters
The paper identifies insufficient evaluation practices in XAI and illustrates the issue using the DetoxAI image-recognition system applied to bias detection and concept unlearning.
Key details
- It provides a human-grounded evaluation example for explanation methods in image classification and examines adapting explanations to evolving data streams subject to concept drift.
- The author discusses adapting counterfactual explanations for drifting data and highlights the challenge of tracking the co-evolution of data, models, and explanations; the 13-page preprint was accepted to the EASi 2026 Workshop (IJCAI-ECAI 2026) and posted 2026-08-06.