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

• URL: http://arxiv.org/abs/2205.08835v1
• Date: Wed, 18 May 2022 10:07:21 GMT
• Title: Fair and Green Hyperparameter Optimization via Multi-objective and Multiple Information Source Bayesian Optimization
• Authors: Antonio Candelieri, Andrea Ponti, Francesco Archetti
• Abstract summary: FanG-HPO uses subsets of the large dataset (aka information sources) to obtain cheap approximations of both accuracy and fairness. Experiments consider two benchmark (fairness) datasets and two machine learning algorithms.
• Score: 0.19116784879310028
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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