# Perfectly Parallel Fairness Certification of Neural Networks

arXiv: https://arxiv.org/abs/1912.02499 · HTML: https://mlsanity.com/paper/1912.02499/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Dec 2019.

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

- [Fair Normalizing Flows](https://mlsanity.com/paper/2106.05937/) (2021)
- [A Review of Formal Methods applied to Machine Learning](https://mlsanity.com/paper/2104.02466/) (2021)
- [An Abstraction-Refinement Approach to Verifying Convolutional Neural Networks](https://mlsanity.com/paper/2201.01978/) (2022)
- [Individual Fairness Guarantees for Neural Networks](https://mlsanity.com/paper/2205.05763/) (2022)
- [Fairify: Fairness Verification of Neural Networks](https://mlsanity.com/paper/2212.06140/) (2022)
- [Neural Network Repair with Reachability Analysis](https://mlsanity.com/paper/2108.04214/) (2021)
- [Scalable Verification of GNN-based Job Schedulers](https://mlsanity.com/paper/2203.03153/) (2022)
- [Latent Space Smoothing for Individually Fair Representations](https://mlsanity.com/paper/2111.13650/) (2021)
- [Verification-Aided Deep Ensemble Selection](https://mlsanity.com/paper/2202.03898/) (2022)
- [CertiFair: A Framework for Certified Global Fairness of Neural Networks](https://mlsanity.com/paper/2205.09927/) (2022)
- [FairProof : Confidential and Certifiable Fairness for Neural Networks](https://mlsanity.com/paper/2402.12572/) (2024)
- [Certifying Some Distributional Fairness with Subpopulation Decomposition](https://mlsanity.com/paper/2205.15494/) (2022)
- [Verifying Generalization in Deep Learning](https://mlsanity.com/paper/2302.05745/) (2023)
- [CAISAR: A platform for Characterizing Artificial Intelligence Safety and Robustness](https://mlsanity.com/paper/2206.03044/) (2022)
- [Neural Network Verification with PyRAT](https://mlsanity.com/paper/2410.23903/) (2024)
- [Provable Fairness for Neural Network Models using Formal Verification](https://mlsanity.com/paper/2212.08578/) (2022)
- [Specifying and Testing k-Safety Properties for Machine-Learning Models](https://mlsanity.com/paper/2206.06054/) (2022)
- [Explainable Global Fairness Verification of Tree-Based Classifiers](https://mlsanity.com/paper/2209.13179/) (2022)
- [Fair Training of Decision Tree Classifiers](https://mlsanity.com/paper/2101.00909/) (2021)
- [Self-Correcting Neural Networks For Safe Classification](https://mlsanity.com/paper/2107.11445/) (2021)
- [FETA: Fairness Enforced Verifying, Training, and Predicting Algorithms for Neural Networks](https://mlsanity.com/paper/2206.00553/) (2022)
- [Precise and Generalized Robustness Certification for Neural Networks](https://mlsanity.com/paper/2306.06747/) (2023)
- [Solving Probabilistic Verification Problems of Neural Networks using Branch and Bound](https://mlsanity.com/paper/2405.17556/) (2024)
- [FairQuant: Certifying and Quantifying Fairness of Deep Neural Networks](https://mlsanity.com/paper/2409.03220/) (2024)
- [DelBugV: Delta-Debugging Neural Network Verifiers](https://mlsanity.com/paper/2305.18558/) (2023)
- [Monitoring Robustness and Individual Fairness](https://mlsanity.com/paper/2506.00496/) (2025)
- [Verifying the Generalization of Deep Learning to Out-of-Distribution Domains](https://mlsanity.com/paper/2406.02024/) (2024)
- [Verifying Attention Robustness of Deep Neural Networks against Semantic Perturbations](https://mlsanity.com/paper/2207.05902/) (2022)
- [Inferring Data Preconditions from Deep Learning Models for Trustworthy Prediction in Deployment](https://mlsanity.com/paper/2401.14628/) (2024)
- [Correct-By-Construction: Certified Individual Fairness through Neural Network Training](https://mlsanity.com/paper/2508.15642/) (2025)

## Abstract (arXiv)

Recently, there is growing concern that machine-learning models, which currently assist or even automate decision making, reproduce, and in the worst case reinforce, bias of the training data. The development of tools and techniques for certifying fairness of these models or describing their biased behavior is, therefore, critical. In this paper, we propose a perfectly parallel static analysis for certifying causal fairness of feed-forward neural networks used for classification of tabular data. When certification succeeds, our approach provides definite guarantees, otherwise, it describes and quantifies the biased behavior. We design the analysis to be sound, in practice also exact, and configurable in terms of scalability and precision, thereby enabling pay-as-you-go certification. We implement our approach in an open-source tool and demonstrate its effectiveness on models trained with popular datasets.