# XAudit : A Theoretical Look at Auditing with Explanations

arXiv: https://arxiv.org/abs/2206.04740 · HTML: https://mlsanity.com/paper/2206.04740/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 49 references that are in the map.
- First posted on arXiv 9 Jun 2022.

## Builds on (most cited first, 14 of 14)

- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [End to End Learning for Self-Driving Cars](https://mlsanity.com/paper/1604.07316/) (2016)
- [Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing](https://mlsanity.com/paper/2001.00973/) (2020)
- [Towards Fast Computation of Certified Robustness for ReLU Networks](https://mlsanity.com/paper/1804.09699/) (2018)
- [Auditing Differentially Private Machine Learning: How Private is Private SGD?](https://mlsanity.com/paper/2006.07709/) (2020)
- [Explainable Machine Learning in Deployment](https://mlsanity.com/paper/1909.06342/) (2019)
- [Model-Agnostic Counterfactual Explanations for Consequential Decisions](https://mlsanity.com/paper/1905.11190/) (2019)
- [Explainable AI for Trees: From Local Explanations to Global Understanding](https://mlsanity.com/paper/1905.04610/) (2019)
- [Framework for Evaluating Faithfulness of Local Explanations](https://mlsanity.com/paper/2202.00734/) (2022)
- [Inverse Classification for Comparison-based Interpretability in Machine Learning](https://mlsanity.com/paper/1712.08443/) (2017)
- [Have you forgotten? A method to assess if machine learning models have forgotten data](https://mlsanity.com/paper/2004.10129/) (2020)
- [EMA: Auditing Data Removal from Trained Models](https://mlsanity.com/paper/2109.03675/) (2021)
- [On the Explanatory Power of Decision Trees](https://mlsanity.com/paper/2108.05266/) (2021)

## Built on by (most cited first, 5 of 5)

- [Model Reconstruction Using Counterfactual Explanations: A Perspective From Polytope Theory](https://mlsanity.com/paper/2405.05369/) (2024)
- [Intrinsic User-Centric Interpretability through Global Mixture of Experts](https://mlsanity.com/paper/2402.02933/) (2024)
- [Robust ML Auditing using Prior Knowledge](https://mlsanity.com/paper/2505.04796/) (2025)
- [Under manipulations, are some AI models harder to audit?](https://mlsanity.com/paper/2402.09043/) (2024)
- [ExpProof : Operationalizing Explanations for Confidential Models with ZKPs](https://mlsanity.com/paper/2502.03773/) (2025)

## Abstract (arXiv)

Responsible use of machine learning requires models to be audited for undesirable properties. While a body of work has proposed using explanations for auditing, how to do so and why has remained relatively ill-understood. This work formalizes the role of explanations in auditing and investigates if and how model explanations can help audits. Specifically, we propose explanation-based algorithms for auditing linear classifiers and decision trees for feature sensitivity. Our results illustrate that Counterfactual explanations are extremely helpful for auditing. While Anchors and decision paths may not be as beneficial in the worst-case, in the average-case they do aid a lot.