# Diversity Drives Fairness: Ensemble of Higher Order Mutants for Intersectional Fairness of Machine Learning Software

arXiv: https://arxiv.org/abs/2412.08167 · HTML: https://mlsanity.com/paper/2412.08167/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 66 references that are in the map.
- First posted on arXiv 11 Dec 2024.

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

- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [Mitigating Unwanted Biases with Adversarial Learning](https://mlsanity.com/paper/1801.07593/) (2018)
- [Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness](https://mlsanity.com/paper/1711.05144/) (2017)
- [Fairness Testing: Testing Software for Discrimination](https://mlsanity.com/paper/1709.03221/) (2017)
- [Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey](https://mlsanity.com/paper/2207.07068/) (2022)
- [Bias in Machine Learning Software: Why? How? What to do?](https://mlsanity.com/paper/2105.12195/) (2021)
- [Fairness Testing: A Comprehensive Survey and Analysis of Trends](https://mlsanity.com/paper/2207.10223/) (2022)
- [Towards Intersectionality in Machine Learning: Including More Identities, Handling Underrepresentation, and Performing Evaluation](https://mlsanity.com/paper/2205.04610/) (2022)
- [Collaboration Challenges in Building ML-Enabled Systems: Communication, Documentation, Engineering, and Process](https://mlsanity.com/paper/2110.10234/) (2021)
- [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)
- [Fair Preprocessing: Towards Understanding Compositional Fairness of Data Transformers in Machine Learning Pipeline](https://mlsanity.com/paper/2106.06054/) (2021)
- [Characterizing Intersectional Group Fairness with Worst-Case Comparisons](https://mlsanity.com/paper/2101.01673/) (2021)
- [NeuronFair: Interpretable White-Box Fairness Testing through Biased Neuron Identification](https://mlsanity.com/paper/2112.13214/) (2021)
- [Evaluating Debiasing Techniques for Intersectional Biases](https://mlsanity.com/paper/2109.10441/) (2021)
- [A Comprehensive Empirical Study of Bias Mitigation Methods for Machine Learning Classifiers](https://mlsanity.com/paper/2207.03277/) (2022)
- [Fairify: Fairness Verification of Neural Networks](https://mlsanity.com/paper/2212.06140/) (2022)
- [FairNeuron: Improving Deep Neural Network Fairness with Adversary Games on Selective Neurons](https://mlsanity.com/paper/2204.02567/) (2022)
- [Easy over Hard: A Case Study on Deep Learning](https://mlsanity.com/paper/1703.00133/) (2017)
- [FairMask: Better Fairness via Model-based Rebalancing of Protected Attributes](https://mlsanity.com/paper/2110.01109/) (2021)
- [Adaptive Fairness Improvement Based on Causality Analysis](https://mlsanity.com/paper/2209.07190/) (2022)
- [Towards Understanding Fairness and its Composition in Ensemble Machine Learning](https://mlsanity.com/paper/2212.04593/) (2022)
- [InfoFair: Information-Theoretic Intersectional Fairness](https://mlsanity.com/paper/2105.11069/) (2021)
- [Automatic Fairness Testing of Neural Classifiers through Adversarial Sampling](https://mlsanity.com/paper/2107.08176/) (2021)
- [Non-functional Requirements for Machine Learning: Understanding Current Use and Challenges in Industry](https://mlsanity.com/paper/2109.00872/) (2021)
- [Fix Fairness, Don't Ruin Accuracy: Performance Aware Fairness Repair using AutoML](https://mlsanity.com/paper/2306.09297/) (2023)
- [Software Fairness: An Analysis and Survey](https://mlsanity.com/paper/2205.08809/) (2022)
- [TESTSGD: Interpretable Testing of Neural Networks Against Subtle Group Discrimination](https://mlsanity.com/paper/2208.11321/) (2022)
- [Social Diversity Reduces the Complexity and Cost of Fostering Fairness](https://mlsanity.com/paper/2211.10517/) (2022)
- [Bias Behind the Wheel: Fairness Testing of Autonomous Driving Systems](https://mlsanity.com/paper/2308.02935/) (2023)
- [Causality-Aided Trade-off Analysis for Machine Learning Fairness](https://mlsanity.com/paper/2305.13057/) (2023)

## Built on by (most cited first, 3 of 3)

- [Rethinking Autonomy: Preventing Failures in AI-Driven Software Engineering](https://mlsanity.com/paper/2508.11824/) (2025)
- [Fairness Is Not Just Ethical: Performance Trade-Off via Data Correlation Tuning to Mitigate Bias in ML Software](https://mlsanity.com/paper/2512.21348/) (2025)
- [Mitigating Bias in Large Vision-Language Models via Counterfactual Ensemble Decoding](https://mlsanity.com/paper/2608.21415/) (2026)

## Abstract (arXiv)

Intersectional fairness is a critical requirement for Machine Learning (ML) software, demanding fairness across subgroups defined by multiple protected attributes. This paper introduces FairHOME, a novel ensemble approach using higher order mutation of inputs to enhance intersectional fairness of ML software during the inference phase. Inspired by social science theories highlighting the benefits of diversity, FairHOME generates mutants representing diverse subgroups for each input instance, thus broadening the array of perspectives to foster a fairer decision-making process. Unlike conventional ensemble methods that combine predictions made by different models, FairHOME combines predictions for the original input and its mutants, all generated by the same ML model, to reach a final decision. Notably, FairHOME is even applicable to deployed ML software as it bypasses the need for training new models. We extensively evaluate FairHOME against seven state-of-the-art fairness improvement methods across 24 decision-making tasks using widely adopted metrics. FairHOME consistently outperforms existing methods across all metrics considered. On average, it enhances intersectional fairness by 47.5%, surpassing the currently best-performing method by 9.6 percentage points.