# NeuronFair: Interpretable White-Box Fairness Testing through Biased Neuron Identification

arXiv: https://arxiv.org/abs/2112.13214 · HTML: https://mlsanity.com/paper/2112.13214/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 65 references that are in the map.
- First posted on arXiv 25 Dec 2021.

## Builds on (most cited first, 30 of 32)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [Adversarial examples in the physical world](https://mlsanity.com/paper/1607.02533/) (2016)
- [Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)](https://mlsanity.com/paper/1711.11279/) (2017)
- [Counterfactual Fairness](https://mlsanity.com/paper/1703.06856/) (2017)
- [ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models](https://mlsanity.com/paper/1708.03999/) (2017)
- [Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models](https://mlsanity.com/paper/1712.04248/) (2017)
- [TextBugger: Generating Adversarial Text Against Real-world Applications](https://mlsanity.com/paper/1812.05271/) (2018)
- [AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias](https://mlsanity.com/paper/1810.01943/) (2018)
- [DeepXplore: Automated Whitebox Testing of Deep Learning Systems](https://mlsanity.com/paper/1705.06640/) (2017)
- [Towards Fairness in Visual Recognition: Effective Strategies for Bias Mitigation](https://mlsanity.com/paper/1911.11834/) (2019)
- [Fairness Testing: Testing Software for Discrimination](https://mlsanity.com/paper/1709.03221/) (2017)
- [Fair Attribute Classification through Latent Space De-biasing](https://mlsanity.com/paper/2012.01469/) (2020)
- [Fairness in Deep Learning: A Computational Perspective](https://mlsanity.com/paper/1908.08843/) (2019)
- [Automated Directed Fairness Testing](https://mlsanity.com/paper/1807.00468/) (2018)
- [Do the Machine Learning Models on a Crowd Sourced Platform Exhibit Bias? An Empirical Study on Model Fairness](https://mlsanity.com/paper/2005.12379/) (2020)
- [FlipTest: Fairness Testing via Optimal Transport](https://mlsanity.com/paper/1906.09218/) (2019)
- [Fair Preprocessing: Towards Understanding Compositional Fairness of Data Transformers in Machine Learning Pipeline](https://mlsanity.com/paper/2106.06054/) (2021)
- [DeepPayload: Black-box Backdoor Attack on Deep Learning Models through Neural Payload Injection](https://mlsanity.com/paper/2101.06896/) (2021)
- [SirenAttack: Generating Adversarial Audio for End-to-End Acoustic Systems](https://mlsanity.com/paper/1901.07846/) (2019)
- [Exploring the Space of Adversarial Images](https://mlsanity.com/paper/1510.05328/) (2015)
- [Automated Test Generation to Detect Individual Discrimination in AI Models](https://mlsanity.com/paper/1809.03260/) (2018)
- [Robustness of on-device Models: Adversarial Attack to Deep Learning Models on Android Apps](https://mlsanity.com/paper/2101.04401/) (2021)
- [Iterative Orthogonal Feature Projection for Diagnosing Bias in Black-Box Models](https://mlsanity.com/paper/1611.04967/) (2016)
- [Deep Learning & Software Engineering: State of Research and Future Directions](https://mlsanity.com/paper/2009.08525/) (2020)
- [Complex Network Classification with Convolutional Neural Network](https://mlsanity.com/paper/1802.00539/) (2018)
- [Measuring Discrimination to Boost Comparative Testing for Multiple Deep Learning Models](https://mlsanity.com/paper/2103.04333/) (2021)

## Built on by (most cited first, 30 of 42)

- [GenderBias-VL: Benchmarking Gender Bias in Vision Language Models via Counterfactual Probing](https://mlsanity.com/paper/2407.00600/) (2024)
- [A Comprehensive Empirical Study of Bias Mitigation Methods for Machine Learning Classifiers](https://mlsanity.com/paper/2207.03277/) (2022)
- [Latent Imitator: Generating Natural Individual Discriminatory Instances for Black-Box Fairness Testing](https://mlsanity.com/paper/2305.11602/) (2023)
- [Fairify: Fairness Verification of Neural Networks](https://mlsanity.com/paper/2212.06140/) (2022)
- [Motif-Backdoor: Rethinking the Backdoor Attack on Graph Neural Networks via Motifs](https://mlsanity.com/paper/2210.13710/) (2022)
- [Towards Understanding Fairness and its Composition in Ensemble Machine Learning](https://mlsanity.com/paper/2212.04593/) (2022)
- [A Critical Survey on Fairness Benefits of Explainable AI](https://mlsanity.com/paper/2310.13007/) (2023)
- [Towards Fair Machine Learning Software: Understanding and Addressing Model Bias Through Counterfactual Thinking](https://mlsanity.com/paper/2302.08018/) (2023)
- [FAIRER: Fairness as Decision Rationale Alignment](https://mlsanity.com/paper/2306.15299/) (2023)
- [NeuFair: Neural Network Fairness Repair with Dropout](https://mlsanity.com/paper/2407.04268/) (2024)
- [Fairness Testing of Large Language Models in Role-Playing](https://mlsanity.com/paper/2411.00585/) (2024)
- [Information-Theoretic Testing and Debugging of Fairness Defects in Deep Neural Networks](https://mlsanity.com/paper/2304.04199/) (2023)
- ["Is your explanation stable?": A Robustness Evaluation Framework for Feature Attribution](https://mlsanity.com/paper/2209.01782/) (2022)
- [FairZK: A Scalable System to Prove Machine Learning Fairness in Zero-Knowledge](https://mlsanity.com/paper/2505.07997/) (2025)
- [CertPri: Certifiable Prioritization for Deep Neural Networks via Movement Cost in Feature Space](https://mlsanity.com/paper/2307.09375/) (2023)
- [FairQuant: Certifying and Quantifying Fairness of Deep Neural Networks](https://mlsanity.com/paper/2409.03220/) (2024)
- [MAFT: Efficient Model-Agnostic Fairness Testing for Deep Neural Networks via Zero-Order Gradient Search](https://mlsanity.com/paper/2412.20086/) (2024)
- [Diversity Drives Fairness: Ensemble of Higher Order Mutants for Intersectional Fairness of Machine Learning Software](https://mlsanity.com/paper/2412.08167/) (2024)
- [FairSense: Long-Term Fairness Analysis of ML-Enabled Systems](https://mlsanity.com/paper/2501.01665/) (2025)
- [Is Multi-Modal Necessarily Better? Robustness Evaluation of Multi-modal Fake News Detection](https://mlsanity.com/paper/2206.08788/) (2022)
- [Enhanced Fairness Testing via Generating Effective Initial Individual Discriminatory Instances](https://mlsanity.com/paper/2209.08321/) (2022)
- [Fairness Testing through Extreme Value Theory](https://mlsanity.com/paper/2501.11597/) (2025)
- [CILIATE: Towards Fairer Class-based Incremental Learning by Dataset and Training Refinement](https://mlsanity.com/paper/2304.04222/) (2023)
- [DREAM: Debugging and Repairing AutoML Pipelines](https://mlsanity.com/paper/2401.00379/) (2023)
- [Software Fairness Dilemma: Is Bias Mitigation a Zero-Sum Game?](https://mlsanity.com/paper/2508.03323/) (2025)
- [Correct-By-Construction: Certified Individual Fairness through Neural Network Training](https://mlsanity.com/paper/2508.15642/) (2025)
- [Causally Perturbed Fairness Testing](https://mlsanity.com/paper/2510.18719/) (2025)
- [Uncovering Discrimination Clusters: Quantifying and Explaining Systematic Fairness Violations](https://mlsanity.com/paper/2512.23769/) (2025)
- [Analyzing Fairness of Neural Network Prediction via Counterfactual Dataset Generation](https://mlsanity.com/paper/2602.10457/) (2026)
- [An Automata-Theoretic Approach to Synthesizing Binarized Neural Networks](https://mlsanity.com/paper/2307.15907/) (2023)

## Abstract (arXiv)

Deep neural networks (DNNs) have demonstrated their outperformance in various domains. However, it raises a social concern whether DNNs can produce reliable and fair decisions especially when they are applied to sensitive domains involving valuable resource allocation, such as education, loan, and employment. It is crucial to conduct fairness testing before DNNs are reliably deployed to such sensitive domains, i.e., generating as many instances as possible to uncover fairness violations. However, the existing testing methods are still limited from three aspects: interpretability, performance, and generalizability. To overcome the challenges, we propose NeuronFair, a new DNN fairness testing framework that differs from previous work in several key aspects: (1) interpretable - it quantitatively interprets DNNs' fairness violations for the biased decision; (2) effective - it uses the interpretation results to guide the generation of more diverse instances in less time; (3) generic - it can handle both structured and unstructured data. Extensive evaluations across 7 datasets and the corresponding DNNs demonstrate NeuronFair's superior performance. For instance, on structured datasets, it generates much more instances (~x5.84) and saves more time (with an average speedup of 534.56%) compared with the state-of-the-art methods. Besides, the instances of NeuronFair can also be leveraged to improve the fairness of the biased DNNs, which helps build more fair and trustworthy deep learning systems.