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

arXiv: https://arxiv.org/abs/2410.21129 · HTML: https://mlsanity.com/paper/2410.21129/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 32 references that are in the map.
- First posted on arXiv 28 Oct 2024.

## Builds on (most cited first, 17 of 17)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods](https://mlsanity.com/paper/1910.09457/) (2019)
- [A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification](https://mlsanity.com/paper/2107.07511/) (2021)
- [Explaining Explanations: An Overview of Interpretability of Machine Learning](https://mlsanity.com/paper/1806.00069/) (2018)
- [Conformal prediction beyond exchangeability](https://mlsanity.com/paper/2202.13415/) (2022)
- [Counterfactual Visual Explanations](https://mlsanity.com/paper/1904.07451/) (2019)
- [An Overview of Machine Teaching](https://mlsanity.com/paper/1801.05927/) (2018)
- [With Malice Towards None: Assessing Uncertainty via Equalized Coverage](https://mlsanity.com/paper/1908.05428/) (2019)
- [Post-hoc explanation of black-box classifiers using confident itemsets](https://mlsanity.com/paper/2005.01992/) (2020)
- [Computationally efficient versions of conformal predictive distributions](https://mlsanity.com/paper/1911.00941/) (2019)
- [Calibrated Explanations: with Uncertainty Information and Counterfactuals](https://mlsanity.com/paper/2305.02305/) (2023)
- [Equal Opportunity of Coverage in Fair Regression](https://mlsanity.com/paper/2311.02243/) (2023)
- [Counterfactual Explanation Based on Gradual Construction for Deep Networks](https://mlsanity.com/paper/2008.01897/) (2020)
- [Visual explanation of black-box model: Similarity Difference and Uniqueness (SIDU) method](https://mlsanity.com/paper/2101.10710/) (2021)
- [Calibrated Explanations for Regression](https://mlsanity.com/paper/2308.16245/) (2023)
- [A method for ethical AI in Defence: A case study on developing trustworthy autonomous systems](https://mlsanity.com/paper/2206.10769/) (2022)

## Built on by (most cited first, 1 of 1)

- [Informative Perturbation Selection for Uncertainty-Aware Post-hoc Explanations](https://mlsanity.com/paper/2603.14894/) (2026)

## Abstract (arXiv)

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.