# Model Agnostic Local Explanations of Reject

arXiv: https://arxiv.org/abs/2205.07623 · HTML: https://mlsanity.com/paper/2205.07623/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 21 references that are in the map.
- First posted on arXiv 16 May 2022.

## Builds on (most cited first, 9 of 9)

- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR](https://mlsanity.com/paper/1711.00399/) (2017)
- [Deep Reinforcement Learning framework for Autonomous Driving](https://mlsanity.com/paper/1704.02532/) (2017)
- [Interpretable Counterfactual Explanations Guided by Prototypes](https://mlsanity.com/paper/1907.02584/) (2019)
- [Machine Learning with a Reject Option: A survey](https://mlsanity.com/paper/2107.11277/) (2021)
- [Convex Density Constraints for Computing Plausible Counterfactual Explanations](https://mlsanity.com/paper/2002.04862/) (2020)
- [Examining Deep Learning Architectures for Crime Classification and Prediction](https://mlsanity.com/paper/1812.00602/) (2018)
- [Explaining Reject Options of Learning Vector Quantization Classifiers](https://mlsanity.com/paper/2202.07244/) (2022)
- [Convex optimization for actionable & plausible counterfactual explanations](https://mlsanity.com/paper/2105.07630/) (2021)

## Built on by (most cited first, 7 of 7)

- ["Even if ..." -- Diverse Semifactual Explanations of Reject](https://mlsanity.com/paper/2207.01898/) (2022)
- [Calibrated Explanations for Regression](https://mlsanity.com/paper/2308.16245/) (2023)
- [Precision and Recall Reject Curves for Classification](https://mlsanity.com/paper/2308.08381/) (2023)
- [Model Agnostic Explainable Selective Regression via Uncertainty Estimation](https://mlsanity.com/paper/2311.09145/) (2023)
- [Stacked Confusion Reject Plots (SCORE)](https://mlsanity.com/paper/2406.17346/) (2024)
- [Abstaining Machine Learning -- Philosophical Considerations](https://mlsanity.com/paper/2409.00706/) (2024)
- [Concisely Explaining the Doubt: Minimum-Size Abductive Explanations for Linear Models with a Reject Option](https://mlsanity.com/paper/2603.14096/) (2026)

## Abstract (arXiv)

The application of machine learning based decision making systems in safety critical areas requires reliable high certainty predictions. Reject options are a common way of ensuring a sufficiently high certainty of predictions made by the system. While being able to reject uncertain samples is important, it is also of importance to be able to explain why a particular sample was rejected. However, explaining general reject options is still an open problem. We propose a model agnostic method for locally explaining arbitrary reject options by means of interpretable models and counterfactual explanations.