# AdaFair: Cumulative Fairness Adaptive Boosting

arXiv: https://arxiv.org/abs/1909.08982 · HTML: https://mlsanity.com/paper/1909.08982/

## Key facts

- Cited by 45 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Sep 2019.

## Built on by (most cited first, 30 of 45)

- [A survey on datasets for fairness-aware machine learning](https://mlsanity.com/paper/2110.00530/) (2021)
- [Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey](https://mlsanity.com/paper/2207.07068/) (2022)
- [Data Collection and Quality Challenges in Deep Learning: A Data-Centric AI Perspective](https://mlsanity.com/paper/2112.06409/) (2021)
- [FairGBM: Gradient Boosting with Fairness Constraints](https://mlsanity.com/paper/2209.07850/) (2022)
- [Training Unbiased Diffusion Models From Biased Dataset](https://mlsanity.com/paper/2403.01189/) (2024)
- [Multi-dimensional discrimination in Law and Machine Learning -- A comparative overview](https://mlsanity.com/paper/2302.05995/) (2023)
- [Boosted CVaR Classification](https://mlsanity.com/paper/2110.13948/) (2021)
- [FORML: Learning to Reweight Data for Fairness](https://mlsanity.com/paper/2202.01719/) (2022)
- [Survey on Fairness Notions and Related Tensions](https://mlsanity.com/paper/2209.13012/) (2022)
- [AIM: Attributing, Interpreting, Mitigating Data Unfairness](https://mlsanity.com/paper/2406.08819/) (2024)
- [Online Fairness-Aware Learning with Imbalanced Data Streams](https://mlsanity.com/paper/2108.06231/) (2021)
- [FairGrad: Fairness Aware Gradient Descent](https://mlsanity.com/paper/2206.10923/) (2022)
- [Disentangling and Operationalizing AI Fairness at LinkedIn](https://mlsanity.com/paper/2306.00025/) (2023)
- [Falcon: Fair Active Learning using Multi-armed Bandits](https://mlsanity.com/paper/2401.12722/) (2024)
- [Evaluation of group fairness measures in student performance prediction problems](https://mlsanity.com/paper/2208.10625/) (2022)
- [Power of Explanations: Towards automatic debiasing in hate speech detection](https://mlsanity.com/paper/2209.09975/) (2022)
- [Fairness and Sequential Decision Making: Limits, Lessons, and Opportunities](https://mlsanity.com/paper/2301.05753/) (2023)
- [Fair Oversampling Technique using Heterogeneous Clusters](https://mlsanity.com/paper/2305.13875/) (2023)
- [Synthetic Tabular Data Generation for Class Imbalance and Fairness: A Comparative Study](https://mlsanity.com/paper/2409.05215/) (2024)
- [Multi-fairness under class-imbalance](https://mlsanity.com/paper/2104.13312/) (2021)
- [Survey on AI Ethics: A Socio-technical Perspective](https://mlsanity.com/paper/2311.17228/) (2023)
- [Fairness without Demographics through Learning Graph of Gradients](https://mlsanity.com/paper/2412.03706/) (2024)
- [IB-GAN: A Unified Approach for Multivariate Time Series Classification under Class Imbalance](https://mlsanity.com/paper/2110.07460/) (2021)
- [Parity-based Cumulative Fairness-aware Boosting](https://mlsanity.com/paper/2201.01148/) (2022)
- [Context matters for fairness -- a case study on the effect of spatial distribution shifts](https://mlsanity.com/paper/2206.11436/) (2022)
- [AdaCC: Cumulative Cost-Sensitive Boosting for Imbalanced Classification](https://mlsanity.com/paper/2209.08309/) (2022)
- [Improving Fairness with Ensemble Combination: Margin-Dependent Bounds](https://mlsanity.com/paper/2301.10813/) (2023)
- [Bias Mitigation in Fine-tuning Pre-trained Models for Enhanced Fairness and Efficiency](https://mlsanity.com/paper/2403.00625/) (2024)
- [An Adaptive Boosting Technique to Mitigate Popularity Bias in Recommender System](https://mlsanity.com/paper/2109.05677/) (2021)
- [A statistical approach to detect sensitive features in a group fairness setting](https://mlsanity.com/paper/2305.06994/) (2023)

## Abstract (arXiv)

The widespread use of ML-based decision making in domains with high societal impact such as recidivism, job hiring and loan credit has raised a lot of concerns regarding potential discrimination. In particular, in certain cases it has been observed that ML algorithms can provide different decisions based on sensitive attributes such as gender or race and therefore can lead to discrimination. Although, several fairness-aware ML approaches have been proposed, their focus has been largely on preserving the overall classification accuracy while improving fairness in predictions for both protected and non-protected groups (defined based on the sensitive attribute(s)). The overall accuracy however is not a good indicator of performance in case of class imbalance, as it is biased towards the majority class. As we will see in our experiments, many of the fairness-related datasets suffer from class imbalance and therefore, tackling fairness requires also tackling the imbalance problem. To this end, we propose AdaFair, a fairness-aware classifier based on AdaBoost that further updates the weights of the instances in each boosting round taking into account a cumulative notion of fairness based upon all current ensemble members, while explicitly tackling class-imbalance by optimizing the number of ensemble members for balanced classification error. Our experiments show that our approach can achieve parity in true positive and true negative rates for both protected and non-protected groups, while it significantly outperforms existing fairness-aware methods up to 25% in terms of balanced error.