VeriX: Towards Verified Explainability of Deep Neural Networks

Min Wu; Haoze Wu; Clark Barrett

NeurIPS 2023poster25 citations

VeriX: Towards Verified Explainability of Deep Neural Networks

Abstract

We present **VeriX** (**Veri**fied e**X**plainability), a system for producing *optimal robust explanations* and generating *counterfactuals* along decision boundaries of machine learning models. We build such explanations and counterfactuals iteratively using constraint solving techniques and a heuristic based on feature-level sensitivity ranking. We evaluate our method on image recognition benchmarks and a real-world scenario of autonomous aircraft taxiing.

trustworthy machine learningdeep neural networksexplainabilityinterpretabilityformal methodsautomated verification

BibTeX

@inproceedings{
wu2023verix,
title={VeriX: Towards Verified Explainability of Deep Neural Networks},
author={Min Wu and Haoze Wu and Clark Barrett},
booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
year={2023},
url={https://openreview.net/forum?id=E2TJI6CKm0}
}