Bayesian Optimization with Fairness Constraints
Bayesian Optimization with Fairness Constraints
复制标题
具有公平性约束的贝叶斯优化
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Michele Donini
中科院分区：
文献类型：
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作者：
Valerio Perrone;Michele Donini
鉴于机器学习在我们生活中的重要性越来越大，以及对算法公平性的需求，已经提出了几种方法来衡量和减轻机器学习模型中的偏差。通常，这些技术是应用于单一类型模型和特定公平定义的专门方法，限制了它们在实践中的有效性。在本文中，我们提出了一个通用的约束贝叶斯优化（BO）框架，以优化任何黑盒机器学习模型的性能，同时执行公平性约束。BO是一类全局优化算法，已成功应用于自动调整机器学习模型的超参数。我们将带有公平约束的BO应用于一系列流行的模型，包括随机森林、梯度增强和神经网络，表明我们可以通过仅对超参数采取行动来获得准确和公平的解决方案。我们还通过经验表明，我们的方法与在训练过程中明确执行公平性约束的专业技术相比具有竞争力，并且优于学习输入数据的无偏表示的预处理方法。
Given the increasing importance of machine learning in our lives and the need for algorithmic fairness, several methods have been proposed to measure and mitigate biases in machine learning models. Commonly, these techniques are specialized approaches applied to a single type of model and a specific definition of fairness, limiting their effectiveness in practice. In this paper, we present a general constrained Bayesian optimization (BO) framework to optimize the performance of any black-box machine learning model while enforcing fairness constraints. BO is a class of global optimization algorithms that has been successfully applied to automatically tune the hyperparameters of machine learning models. We apply BO with fairness constraints to a range of popular models, including random forests, gradient boosting and neural networks, showing that we can obtain accurate and fair solutions by acting solely on the hyperparameters. We also show empirically that our approach is competitive with specialized techniques that explicitly enforce fairness constraints during training, and outperforms preprocessing methods that learn unbiased representations of the input data.