Fair Bayesian Optimization

Perrone, Valerio; Donini, Michele; Zafar, Muhammad Bilal; Schmucker, Robin; Kenthapadi, Krishnaram; Archambeau, Cédric

doi:10.1145/3461702.3462629

Cited by 40 publications

(25 citation statements)

References 16 publications

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“…3) Data-driven fairness-aware Bayesian optimization: Bayesian optimization is a popular and effective data-driven optimization approach [181]. In [182], Perrone et al have proposed a general constrained Bayesian optimization framework which is able to cater different ML models and one or multiple fairness constraints. Following the Bayesian optimization [183], this study iteratively tunes hyperparameters (x) based on the best query (x * ), which is achieved by maximizing an acquisition function.…”

Section: B Fairness In Data-driven Optimizationmentioning

confidence: 99%

Towards Fairness-Aware Multi-Objective Optimization

Yu¹,

Ma²,

Du³

et al. 2022

Preprint

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Recent years have seen the rapid development of fairness-aware machine learning in mitigating unfairness or discrimination in decision-making in a wide range of applications. However, much less attention has been paid to the fairness-aware multi-objective optimization, which is indeed commonly seen in real life, such as fair resource allocation problems and datadriven multi-objective optimization problems. This paper aims to illuminate and broaden our understanding of multi-objective optimization from the perspective of fairness. To this end, we start with a discussion of user preferences in multi-objective optimization and then explore its relationship to fairness in machine learning and multi-objective optimization. Following the above discussions, representative cases of fairness-aware multiobjective optimization are presented, further elaborating the importance of fairness in traditional multi-objective optimization, data-driven optimization and federated optimization. Finally, challenges and opportunities in fairness-aware multi-objective optimization are addressed. We hope that this article makes a small step forward towards understanding fairness in the context of optimization and promote research interest in fairness-aware multi-objective optimization.

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Section: B Fairness In Data-driven Optimizationmentioning

confidence: 99%

Towards Fairness-Aware Multi-Objective Optimization

Yu¹,

Ma²,

Du³

et al. 2022

Preprint

View full text Add to dashboard Cite

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“…Unfortunately the code is not currently available: an implementation was uder review to be included into AutoGluon 1 suite, but it has not been included yet. Other relevant works are (Perrone et al, 2021;2020), where the fairness requirement are modelled as a constraint -instead of an objective -in single-objective (i.e., accuracy maximization) HPO.…”

Section: Related Workmentioning

confidence: 99%

“…These approaches regard the design of fairness-aware (or fair-by-design) ML algorithms, but they suffer from one or more of the following drawbacks (Perrone et al, 2021): the intervention performed to deal with biases is (i) specific to the model class (e.g., linear models only), (ii) limited to a specific definition(s) of fairness, (iii) limited to a single, binary sensitive feature, (iv) requires access to sensitive feature information at prediction time, and (v) results in a randomized classifier that may generate different prediction for the same input at different times.…”

Section: Introduction 1rationale and Motivationsmentioning

confidence: 99%

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

Candelieri¹,

Ponti²,

Archetti³

2022

Preprint

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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 Paretoefficient 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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“…Previous work has extended BO to constrained optimization (Gardner et al, 2014;Gelbart et al, 2014), in which the goal is to optimize a given metric subject to any number of data-dependent constraints. Recently, Perrone et al (2020) applied this approach to the fairness setting by weighing the acquisition function by the likelihood of fulfilling the fairness constraints. However, constrained 1: Number of sampled configurations, n i , and budget per configuration, r i , for each Hyperband bracket (when the ratio of budget increase η = 3, and the maximum budget per configuration R = 100).…”

Section: Related Workmentioning

confidence: 99%

A Bandit-Based Algorithm for Fairness-Aware Hyperparameter Optimization

Cruz,

Saleiro,

Belém

et al. 2020

Preprint

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Considerable research effort has been guided towards algorithmic fairness but there is still no major breakthrough. In practice, an exhaustive search over all possible techniques and hyperparameters is needed to find optimal fairness-accuracy trade-offs. Hence, coupled with the lack of tools for ML practitioners, real-world adoption of bias reduction methods is still scarce. To address this, we present Fairband, a bandit-based fairness-aware hyperparameter optimization (HO) algorithm. Fairband is conceptually simple, resource-efficient, easy to implement, and agnostic to both the objective metrics, model types and the hyperparameter space being explored. Moreover, by introducing fairness notions into HO, we enable seamless and efficient integration of fairness objectives into real-world ML pipelines. We compare Fairband with popular HO methods on four real-world decision-making datasets. We show that Fairband can efficiently navigate the fairness-accuracy tradeoff through hyperparameter optimization. Furthermore, without extra training cost, it consistently finds configurations attaining substantially improved fairness at a comparatively small decrease in predictive accuracy.

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Fair Bayesian Optimization

Cited by 40 publications

References 16 publications

Towards Fairness-Aware Multi-Objective Optimization

Towards Fairness-Aware Multi-Objective Optimization

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

A Bandit-Based Algorithm for Fairness-Aware Hyperparameter Optimization

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