# FARF: A Fair and Adaptive Random Forests Classifier

arXiv: https://arxiv.org/abs/2108.07403 · HTML: https://mlsanity.com/paper/2108.07403/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 49 references that are in the map.
- First posted on arXiv 17 Aug 2021.

## Builds on (most cited first, 18 of 18)

- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [Inherent Trade-Offs in the Fair Determination of Risk Scores](https://mlsanity.com/paper/1609.05807/) (2016)
- [Fairness in Machine Learning: Lessons from Political Philosophy](https://mlsanity.com/paper/1712.03586/) (2017)
- [A Confidence-Based Approach for Balancing Fairness and Accuracy](https://mlsanity.com/paper/1601.05764/) (2016)
- [Putting Fairness Principles into Practice: Challenges, Metrics, and Improvements](https://mlsanity.com/paper/1901.04562/) (2019)
- [FAHT: An Adaptive Fairness-aware Decision Tree Classifier](https://mlsanity.com/paper/1907.07237/) (2019)
- [Mathematical Notions vs. Human Perception of Fairness: A Descriptive Approach to Fairness for Machine Learning](https://mlsanity.com/paper/1902.04783/) (2019)
- [FAE: A Fairness-Aware Ensemble Framework](https://mlsanity.com/paper/2002.00695/) (2020)
- [Disentangled Dynamic Graph Deep Generation](https://mlsanity.com/paper/2010.07276/) (2020)
- [Bayesian Modeling of Intersectional Fairness: The Variance of Bias](https://mlsanity.com/paper/1811.07255/) (2018)
- [A Deterministic Self-Organizing Map Approach and its Application on Satellite Data based Cloud Type Classification](https://mlsanity.com/paper/1808.08315/) (2018)
- [A survey on measuring indirect discrimination in machine learning](https://mlsanity.com/paper/1511.00148/) (2015)
- [Fairness-enhancing interventions in stream classification](https://mlsanity.com/paper/1907.07223/) (2019)
- [Using Machine Learning to Automate Mammogram Images Analysis](https://mlsanity.com/paper/2012.03151/) (2020)
- [LSTM Based Sentiment Analysis for Cryptocurrency Prediction](https://mlsanity.com/paper/2103.14804/) (2021)
- [Cognitive Visual Commonsense Reasoning Using Dynamic Working Memory](https://mlsanity.com/paper/2107.01671/) (2021)
- [A Data-driven Human Responsibility Management System](https://mlsanity.com/paper/2012.03190/) (2020)
- [Responsibility Management through Responsibility Networks](https://mlsanity.com/paper/2102.07246/) (2021)

## Built on by (most cited first, 30 of 37)

- [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)
- [History, Development, and Principles of Large Language Models-An Introductory Survey](https://mlsanity.com/paper/2402.06853/) (2024)
- [Longitudinal Fairness with Censorship](https://mlsanity.com/paper/2203.16024/) (2022)
- [Transferring Fairness under Distribution Shifts via Fair Consistency Regularization](https://mlsanity.com/paper/2206.12796/) (2022)
- [Fairness Amidst Non-IID Graph Data: A Literature Review](https://mlsanity.com/paper/2202.07170/) (2022)
- [FairAIED: Navigating Fairness, Bias, and Ethics in Educational AI Applications](https://mlsanity.com/paper/2407.18745/) (2024)
- [Towards Fair Machine Learning Software: Understanding and Addressing Model Bias Through Counterfactual Thinking](https://mlsanity.com/paper/2302.08018/) (2023)
- [Preventing Discriminatory Decision-making in Evolving Data Streams](https://mlsanity.com/paper/2302.08017/) (2023)
- [How Robust is Your Fairness? Evaluating and Sustaining Fairness under Unseen Distribution Shifts](https://mlsanity.com/paper/2207.01168/) (2022)
- [Individual Fairness under Uncertainty](https://mlsanity.com/paper/2302.08015/) (2023)
- [Fairness Definitions in Language Models Explained](https://mlsanity.com/paper/2407.18454/) (2024)
- [Fair Tree Classifier using Strong Demographic Parity](https://mlsanity.com/paper/2110.09295/) (2021)
- [Incremental Mining of Frequent Serial Episodes Considering Multiple Occurrences](https://mlsanity.com/paper/2201.11650/) (2022)
- [Adapting Static Fairness to Sequential Decision-Making: Bias Mitigation Strategies towards Equal Long-term Benefit Rate](https://mlsanity.com/paper/2309.03426/) (2023)
- [Efficient Algorithms For Fair Clustering with a New Fairness Notion](https://mlsanity.com/paper/2109.00708/) (2021)
- [Sustaining Fairness via Incremental Learning](https://mlsanity.com/paper/2208.12212/) (2022)
- [Generative AI in Depth: A Survey of Recent Advances, Model Variants, and Real-World Applications](https://mlsanity.com/paper/2510.21887/) (2025)
- [FEAMOE: Fair, Explainable and Adaptive Mixture of Experts](https://mlsanity.com/paper/2210.04995/) (2022)
- [Improving Fairness with Ensemble Combination: Margin-Dependent Bounds](https://mlsanity.com/paper/2301.10813/) (2023)
- [AMCR: A Framework for Assessing and Mitigating Copyright Risks in Generative Models](https://mlsanity.com/paper/2509.00641/) (2025)
- [Towards Transparent AI: A Survey on Explainable Language Models](https://mlsanity.com/paper/2509.21631/) (2025)
- [On the Robustness of Random Forest Against Untargeted Data Poisoning: An Ensemble-Based Approach](https://mlsanity.com/paper/2209.14013/) (2022)
- [A Survey on Preserving Fairness Guarantees in Changing Environments](https://mlsanity.com/paper/2211.07530/) (2022)
- [Fairness under Covariate Shift: Improving Fairness-Accuracy tradeoff with few Unlabeled Test Samples](https://mlsanity.com/paper/2310.07535/) (2023)
- [Unveiling Group-Specific Distributed Concept Drift: A Fairness Imperative in Federated Learning](https://mlsanity.com/paper/2402.07586/) (2024)
- [A Generic Knowledge Based Medical Diagnosis Expert System](https://mlsanity.com/paper/2110.04439/) (2021)
- [The Internet of Responsibilities-Connecting Human Responsibilities using Big Data and Blockchain](https://mlsanity.com/paper/2312.04729/) (2023)
- [Uncertain Boundaries: Multidisciplinary Approaches to Copyright Issues in Generative AI](https://mlsanity.com/paper/2404.08221/) (2024)
- [Fair Mixed Effects Support Vector Machine](https://mlsanity.com/paper/2405.06433/) (2024)

## Abstract (arXiv)

As Artificial Intelligence (AI) is used in more applications, the need to consider and mitigate biases from the learned models has followed. Most works in developing fair learning algorithms focus on the offline setting. However, in many real-world applications data comes in an online fashion and needs to be processed on the fly. Moreover, in practical application, there is a trade-off between accuracy and fairness that needs to be accounted for, but current methods often have multiple hyperparameters with non-trivial interaction to achieve fairness. In this paper, we propose a flexible ensemble algorithm for fair decision-making in the more challenging context of evolving online settings. This algorithm, called FARF (Fair and Adaptive Random Forests), is based on using online component classifiers and updating them according to the current distribution, that also accounts for fairness and a single hyperparameters that alters fairness-accuracy balance. Experiments on real-world discriminated data streams demonstrate the utility of FARF.