Calibrated Explanations: with Uncertainty Information and Counterfactuals

التفاصيل البيبلوغرافية
العنوان: Calibrated Explanations: with Uncertainty Information and Counterfactuals
المؤلفون: Lofstrom, Helena, Lofstrom, Tuwe, Johansson, Ulf, Sonstrod, Cecilia
المصدر: H. Lofstrom, T. Lofstrom, U. Johansson, C. Sonstrod, (2024) Calibrated explanations: With uncertainty information and counterfactuals, Expert Systems with Applications, 123154, ISSN 0957-4174
سنة النشر: 2023
المجموعة: Computer Science
مصطلحات موضوعية: Computer Science - Artificial Intelligence, Computer Science - Machine Learning
الوصف: While local explanations for AI models can offer insights into individual predictions, such as feature importance, they are plagued by issues like instability. The unreliability of feature weights, often skewed due to poorly calibrated ML models, deepens these challenges. Moreover, the critical aspect of feature importance uncertainty remains mostly unaddressed in Explainable AI (XAI). The novel feature importance explanation method presented in this paper, called Calibrated Explanations (CE), is designed to tackle these issues head-on. Built on the foundation of Venn-Abers, CE not only calibrates the underlying model but also delivers reliable feature importance explanations with an exact definition of the feature weights. CE goes beyond conventional solutions by addressing output uncertainty. It accomplishes this by providing uncertainty quantification for both feature weights and the model's probability estimates. Additionally, CE is model-agnostic, featuring easily comprehensible conditional rules and the ability to generate counterfactual explanations with embedded uncertainty quantification. Results from an evaluation with 25 benchmark datasets underscore the efficacy of CE, making it stand as a fast, reliable, stable, and robust solution.
Comment: 19 pages, 6 figures, 3 tables, submitted to journal
نوع الوثيقة: Working Paper
DOI: 10.1016/j.eswa.2024.123154
URL الوصول: http://arxiv.org/abs/2305.02305
رقم الأكسشن: edsarx.2305.02305
قاعدة البيانات: arXiv
الوصف
DOI:10.1016/j.eswa.2024.123154