# FairNeuron: Improving Deep Neural Network Fairness with Adversary Games on Selective Neurons

arXiv: https://arxiv.org/abs/2204.02567 · HTML: https://mlsanity.com/paper/2204.02567/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 66 references that are in the map.
- First posted on arXiv 6 Apr 2022.

## Builds on (most cited first, 30 of 31)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [Unsupervised Domain Adaptation by Backpropagation](https://mlsanity.com/paper/1409.7495/) (2014)
- [End to End Learning for Self-Driving Cars](https://mlsanity.com/paper/1604.07316/) (2016)
- [Certifying and removing disparate impact](https://mlsanity.com/paper/1412.3756/) (2014)
- [Fair prediction with disparate impact: A study of bias in recidivism prediction instruments](https://mlsanity.com/paper/1610.07524/) (2016)
- [Mitigating Unwanted Biases with Adversarial Learning](https://mlsanity.com/paper/1801.07593/) (2018)
- [On Fairness and Calibration](https://mlsanity.com/paper/1709.02012/) (2017)
- [Fairness in Machine Learning: A Survey](https://mlsanity.com/paper/2010.04053/) (2020)
- [Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment](https://mlsanity.com/paper/1610.08452/) (2016)
- [Fairness Without Demographics in Repeated Loss Minimization](https://mlsanity.com/paper/1806.08010/) (2018)
- [Fairness in Criminal Justice Risk Assessments: The State of the Art](https://mlsanity.com/paper/1703.09207/) (2017)
- [Fairness without Demographics through Adversarially Reweighted Learning](https://mlsanity.com/paper/2006.13114/) (2020)
- [A comparative study of fairness-enhancing interventions in machine learning](https://mlsanity.com/paper/1802.04422/) (2018)
- [Mitigating Bias in Algorithmic Hiring: Evaluating Claims and Practices](https://mlsanity.com/paper/1906.09208/) (2019)
- [Adversarial Removal of Demographic Attributes from Text Data](https://mlsanity.com/paper/1808.06640/) (2018)
- [Data Decisions and Theoretical Implications when Adversarially Learning Fair Representations](https://mlsanity.com/paper/1707.00075/) (2017)
- [A Convolutional Neural Network for Modelling Sentences](https://mlsanity.com/paper/1404.2188/) (2014)
- [Algorithmic Fairness](https://mlsanity.com/paper/2001.09784/) (2020)
- [Cycles in adversarial regularized learning](https://mlsanity.com/paper/1709.02738/) (2017)
- [Convolutional Neural Network Architectures for Matching Natural Language Sentences](https://mlsanity.com/paper/1503.03244/) (2015)
- [Automated Directed Fairness Testing](https://mlsanity.com/paper/1807.00468/) (2018)
- [Operationalizing Individual Fairness with Pairwise Fair Representations](https://mlsanity.com/paper/1907.01439/) (2019)
- [Fairness GAN](https://mlsanity.com/paper/1805.09910/) (2018)
- [Adversarial Defense Through Network Profiling Based Path Extraction](https://mlsanity.com/paper/1904.08089/) (2019)
- [Achieving non-discrimination in data release](https://mlsanity.com/paper/1611.07438/) (2016)
- [Automated Test Generation to Detect Individual Discrimination in AI Models](https://mlsanity.com/paper/1809.03260/) (2018)
- [Dynamic Slicing for Deep Neural Networks](https://mlsanity.com/paper/2009.13747/) (2020)
- [Best of Both Worlds: Ex-Ante and Ex-Post Fairness in Resource Allocation](https://mlsanity.com/paper/2005.14122/) (2020)

## Built on by (most cited first, 21 of 21)

- [Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey](https://mlsanity.com/paper/2207.07068/) (2022)
- [Memorization in deep learning: A survey](https://mlsanity.com/paper/2406.03880/) (2024)
- [Towards Fair Machine Learning Software: Understanding and Addressing Model Bias Through Counterfactual Thinking](https://mlsanity.com/paper/2302.08018/) (2023)
- [NeuFair: Neural Network Fairness Repair with Dropout](https://mlsanity.com/paper/2407.04268/) (2024)
- [Fix Fairness, Don't Ruin Accuracy: Performance Aware Fairness Repair using AutoML](https://mlsanity.com/paper/2306.09297/) (2023)
- [Causality-Aided Trade-off Analysis for Machine Learning Fairness](https://mlsanity.com/paper/2305.13057/) (2023)
- [Neuron Patching: Semantic-based Neuron-level Language Model Repair for Code Generation](https://mlsanity.com/paper/2312.05356/) (2023)
- [Attention Pruning: Automated Fairness Repair of Language Models via Surrogate Simulated Annealing](https://mlsanity.com/paper/2503.15815/) (2025)
- [FairQuant: Certifying and Quantifying Fairness of Deep Neural Networks](https://mlsanity.com/paper/2409.03220/) (2024)
- [Diversity Drives Fairness: Ensemble of Higher Order Mutants for Intersectional Fairness of Machine Learning Software](https://mlsanity.com/paper/2412.08167/) (2024)
- [A Semantic-based Optimization Approach for Repairing LLMs: Case Study on Code Generation](https://mlsanity.com/paper/2503.12899/) (2025)
- [CILIATE: Towards Fairer Class-based Incremental Learning by Dataset and Training Refinement](https://mlsanity.com/paper/2304.04222/) (2023)
- [Towards Better Fairness-Utility Trade-off: A Comprehensive Measurement-Based Reinforcement Learning Framework](https://mlsanity.com/paper/2307.11379/) (2023)
- [Generating Synthetic Fair Syntax-agnostic Data by Learning and Distilling Fair Representation](https://mlsanity.com/paper/2408.10755/) (2024)
- [Software Fairness Dilemma: Is Bias Mitigation a Zero-Sum Game?](https://mlsanity.com/paper/2508.03323/) (2025)
- [Efficient DNN-Powered Software with Fair Sparse Models](https://mlsanity.com/paper/2407.02805/) (2024)
- [FairFLRep: Fairness aware fault localization and repair of Deep Neural Networks](https://mlsanity.com/paper/2508.08151/) (2025)
- [Shapley-Guided Neural Repair Approach via Derivative-Free Optimization](https://mlsanity.com/paper/2604.00422/) (2026)
- [Fairness Invariants: A Relational Approach to Explaining and Mitigating Fairness Bugs](https://mlsanity.com/paper/2608.26209/) (2026)
- [FairLint-DL: An IDE-Native Tool for Fairness Debugging of Deep Learning Software](https://mlsanity.com/paper/2609.16321/) (2026)
- [FORGE: Verification-Gated Behavioral Repair for Generative Language Models](https://mlsanity.com/paper/2610.05190/) (2026)

## Abstract (arXiv)

With Deep Neural Network (DNN) being integrated into a growing number of critical systems with far-reaching impacts on society, there are increasing concerns on their ethical performance, such as fairness. Unfortunately, model fairness and accuracy in many cases are contradictory goals to optimize. To solve this issue, there has been a number of work trying to improve model fairness by using an adversarial game in model level. This approach introduces an adversary that evaluates the fairness of a model besides its prediction accuracy on the main task, and performs joint-optimization to achieve a balanced result. In this paper, we noticed that when performing backward propagation based training, such contradictory phenomenon has shown on individual neuron level. Based on this observation, we propose FairNeuron, a DNN model automatic repairing tool, to mitigate fairness concerns and balance the accuracy-fairness trade-off without introducing another model. It works on detecting neurons with contradictory optimization directions from accuracy and fairness training goals, and achieving a trade-off by selective dropout. Comparing with state-of-the-art methods, our approach is lightweight, making it scalable and more efficient. Our evaluation on 3 datasets shows that FairNeuron can effectively improve all models' fairness while maintaining a stable utility.