Fast Calibrated Explanations: Efficient and Uncertainty-Aware Explanations for Machine Learning Models

TL;DR

This paper presents Fast Calibrated Explanations, a method that offers rapid, uncertainty-aware explanations for machine learning models by integrating perturbation techniques, enabling real-time probabilistic insights with a trade-off in explanation detail.

Contribution

It introduces a fast, efficient explanation method that incorporates uncertainty quantification, extending Calibrated Explanations for real-time, high-stakes applications.

Findings

Significant speedup in explanation generation

Maintains uncertainty quantification in explanations

Applicable to classification and probabilistic regression

Abstract

This paper introduces Fast Calibrated Explanations, a method designed for generating rapid, uncertainty-aware explanations for machine learning models. By incorporating perturbation techniques from ConformaSight - a global explanation framework - into the core elements of Calibrated Explanations (CE), we achieve significant speedups. These core elements include local feature importance with calibrated predictions, both of which retain uncertainty quantification. While the new method sacrifices a small degree of detail, it excels in computational efficiency, making it ideal for high-stakes, real-time applications. Fast Calibrated Explanations are applicable to probabilistic explanations in classification and thresholded regression tasks, where they provide the likelihood of a target being above or below a user-defined threshold. This approach maintains the versatility of CE for both classification and probabilistic regression, making it suitable for a range of predictive tasks where uncertainty quantification is crucial.