# Search-Based Fairness Testing: An Overview

arXiv: https://arxiv.org/abs/2311.06175 · HTML: https://mlsanity.com/paper/2311.06175/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 43 references that are in the map.
- First posted on arXiv 10 Nov 2023.

## Builds on (most cited first, 20 of 20)

- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Machine Learning Testing: Survey, Landscapes and Horizons](https://mlsanity.com/paper/1906.10742/) (2019)
- [Trustworthy AI: From Principles to Practices](https://mlsanity.com/paper/2110.01167/) (2021)
- [A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle](https://mlsanity.com/paper/1901.10002/) (2019)
- [Fairness Testing: Testing Software for Discrimination](https://mlsanity.com/paper/1709.03221/) (2017)
- [FairVis: Visual Analytics for Discovering Intersectional Bias in Machine Learning](https://mlsanity.com/paper/1904.05419/) (2019)
- [Automated Directed Fairness Testing](https://mlsanity.com/paper/1807.00468/) (2018)
- [Software Engineering for AI-Based Systems: A Survey](https://mlsanity.com/paper/2105.01984/) (2021)
- [Fairness Testing: A Comprehensive Survey and Analysis of Trends](https://mlsanity.com/paper/2207.10223/) (2022)
- [NeuronFair: Interpretable White-Box Fairness Testing through Biased Neuron Identification](https://mlsanity.com/paper/2112.13214/) (2021)
- [Accuracy and Fairness Trade-offs in Machine Learning: A Stochastic Multi-Objective Approach](https://mlsanity.com/paper/2008.01132/) (2020)
- [Latent Imitator: Generating Natural Individual Discriminatory Instances for Black-Box Fairness Testing](https://mlsanity.com/paper/2305.11602/) (2023)
- [Trust and Medical AI: The challenges we face and the expertise needed to overcome them](https://mlsanity.com/paper/2008.07734/) (2020)
- [Automatic Fairness Testing of Neural Classifiers through Adversarial Sampling](https://mlsanity.com/paper/2107.08176/) (2021)
- [TESTSGD: Interpretable Testing of Neural Networks Against Subtle Group Discrimination](https://mlsanity.com/paper/2208.11321/) (2022)
- [Fairness Testing of Deep Image Classification with Adequacy Metrics](https://mlsanity.com/paper/2111.08856/) (2021)
- [Explainable Global Fairness Verification of Tree-Based Classifiers](https://mlsanity.com/paper/2209.13179/) (2022)
- [FairRec: Fairness Testing for Deep Recommender Systems](https://mlsanity.com/paper/2304.07030/) (2023)
- [Enhanced Fairness Testing via Generating Effective Initial Individual Discriminatory Instances](https://mlsanity.com/paper/2209.08321/) (2022)
- [fAux: Testing Individual Fairness via Gradient Alignment](https://mlsanity.com/paper/2210.06288/) (2022)

## Abstract (arXiv)

Artificial Intelligence (AI) has demonstrated remarkable capabilities in domains such as recruitment, finance, healthcare, and the judiciary. However, biases in AI systems raise ethical and societal concerns, emphasizing the need for effective fairness testing methods. This paper reviews current research on fairness testing, particularly its application through search-based testing. Our analysis highlights progress and identifies areas of improvement in addressing AI systems biases. Future research should focus on leveraging established search-based testing methodologies for fairness testing.