# Fair and Green Hyperparameter Optimization via Multi-objective and Multiple Information Source Bayesian Optimization

arXiv: https://arxiv.org/abs/2205.08835 · HTML: https://mlsanity.com/paper/2205.08835/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 75 references that are in the map.
- First posted on arXiv 18 May 2022.

## Builds on (most cited first, 28 of 28)

- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Energy and Policy Considerations for Deep Learning in NLP](https://mlsanity.com/paper/1906.02243/) (2019)
- [Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization](https://mlsanity.com/paper/1603.06560/) (2016)
- [Green AI](https://mlsanity.com/paper/1907.10597/) (2019)
- [AutoML: A Survey of the State-of-the-Art](https://mlsanity.com/paper/1908.00709/) (2019)
- [Non-stochastic Best Arm Identification and Hyperparameter Optimization](https://mlsanity.com/paper/1502.07943/) (2015)
- [A comparative study of fairness-enhancing interventions in machine learning](https://mlsanity.com/paper/1802.04422/) (2018)
- [Fairness in Machine Learning](https://mlsanity.com/paper/2012.15816/) (2020)
- [Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization](https://mlsanity.com/paper/2006.05078/) (2020)
- [A survey on datasets for fairness-aware machine learning](https://mlsanity.com/paper/2110.00530/) (2021)
- [Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets](https://mlsanity.com/paper/1605.07079/) (2016)
- [A Flexible Framework for Multi-Objective Bayesian Optimization using Random Scalarizations](https://mlsanity.com/paper/1805.12168/) (2018)
- [Predictive Entropy Search for Multi-objective Bayesian Optimization](https://mlsanity.com/paper/1511.05467/) (2015)
- [Multi-Information Source Optimization](https://mlsanity.com/paper/1603.00389/) (2016)
- [Max-value Entropy Search for Multi-Objective Bayesian Optimization with Constraints](https://mlsanity.com/paper/2009.01721/) (2020)
- [The Parallel Knowledge Gradient Method for Batch Bayesian Optimization](https://mlsanity.com/paper/1606.04414/) (2016)
- [Random Hypervolume Scalarizations for Provable Multi-Objective Black Box Optimization](https://mlsanity.com/paper/2006.04655/) (2020)
- [Multi-objective Bayesian Optimization using Pareto-frontier Entropy](https://mlsanity.com/paper/1906.00127/) (2019)
- [Fair Bayesian Optimization](https://mlsanity.com/paper/2006.05109/) (2020)
- [Uncertainty-Aware Search Framework for Multi-Objective Bayesian Optimization](https://mlsanity.com/paper/2204.05944/) (2022)
- [Cost-aware Bayesian Optimization](https://mlsanity.com/paper/2003.10870/) (2020)
- [Towards Green Automated Machine Learning: Status Quo and Future Directions](https://mlsanity.com/paper/2111.05850/) (2021)
- [A Bayesian approach to constrained single- and multi-objective optimization](https://mlsanity.com/paper/1510.00503/) (2015)
- [Multi-Fidelity Multi-Objective Bayesian Optimization: An Output Space Entropy Search Approach](https://mlsanity.com/paper/2011.01542/) (2020)
- [Achieving Fairness with a Simple Ridge Penalty](https://mlsanity.com/paper/2105.13817/) (2021)
- [Green Machine Learning via Augmented Gaussian Processes and Multi-Information Source Optimization](https://mlsanity.com/paper/2006.14233/) (2020)
- [Fast Exact Computation of Expected HyperVolume Improvement](https://mlsanity.com/paper/1812.07692/) (2018)
- [MISO-wiLDCosts: Multi Information Source Optimization with Location Dependent Costs](https://mlsanity.com/paper/2102.04951/) (2021)

## Built on by (most cited first, 14 of 14)

- [Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey](https://mlsanity.com/paper/2207.07068/) (2022)
- [Efficiently Controlling Multiple Risks with Pareto Testing](https://mlsanity.com/paper/2210.07913/) (2022)
- [Speeding Up Multi-Objective Hyperparameter Optimization by Task Similarity-Based Meta-Learning for the Tree-Structured Parzen Estimator](https://mlsanity.com/paper/2212.06751/) (2022)
- [Mastering the exploration-exploitation trade-off in Bayesian Optimization](https://mlsanity.com/paper/2305.08624/) (2023)
- [Wasserstein Barycenter Gaussian Process based Bayesian Optimization](https://mlsanity.com/paper/2505.12471/) (2025)
- [FigBO: A Generalized Acquisition Function Framework with Look-Ahead Capability for Bayesian Optimization](https://mlsanity.com/paper/2504.20307/) (2025)
- [ABG-NAS: Adaptive Bayesian Genetic Neural Architecture Search for Graph Representation Learning](https://mlsanity.com/paper/2504.21254/) (2025)
- [Vector Optimization with Gaussian Process Bandits](https://mlsanity.com/paper/2412.02484/) (2024)
- [VirnyFlow: Optimizing ML Pipelines for Accuracy, Fairness, and Stability at Scale](https://mlsanity.com/paper/2506.01584/) (2025)
- [Are Trees Really Green? A Detection Approach of IoT Malware Attacks](https://mlsanity.com/paper/2506.07836/) (2025)
- [Weighted Wasserstein Barycenter of Gaussian Processes for exotic Bayesian Optimization tasks](https://mlsanity.com/paper/2602.09181/) (2026)
- [mlr3mbo: Bayesian Optimization in R](https://mlsanity.com/paper/2603.29730/) (2026)
- [Reduced-Space Multi-Fidelity Bayesian Optimization of Process Simulation Models](https://mlsanity.com/paper/2609.17440/) (2026)
- [Energy-aware frugal Bayesian optimization](https://mlsanity.com/paper/2609.31638/) (2026)

## Abstract (arXiv)

There is a consensus that focusing only on accuracy in searching for optimal machine learning models amplifies biases contained in the data, leading to unfair predictions and decision supports. Recently, multi-objective hyperparameter optimization has been proposed to search for machine learning models which offer equally Pareto-efficient trade-offs between accuracy and fairness. Although these approaches proved to be more versatile than fairness-aware machine learning algorithms -- which optimize accuracy constrained to some threshold on fairness -- they could drastically increase the energy consumption in the case of large datasets. In this paper we propose FanG-HPO, a Fair and Green Hyperparameter Optimization (HPO) approach based on both multi-objective and multiple information source Bayesian optimization. FanG-HPO uses subsets of the large dataset (aka information sources) to obtain cheap approximations of both accuracy and fairness, and multi-objective Bayesian Optimization to efficiently identify Pareto-efficient machine learning models. Experiments consider two benchmark (fairness) datasets and two machine learning algorithms (XGBoost and Multi-Layer Perceptron), and provide an assessment of FanG-HPO against both fairness-aware machine learning algorithms and hyperparameter optimization via a multi-objective single-source optimization algorithm in BoTorch, a state-of-the-art platform for Bayesian Optimization.