# The cost of fairness in classification

@article{Menon2017TheCO, title={The cost of fairness in classification}, author={Aditya Krishna Menon and Robert C. Williamson}, journal={ArXiv}, year={2017}, volume={abs/1705.09055} }

We study the problem of learning classifiers with a fairness constraint, with three main contributions towards the goal of quantifying the problem's inherent tradeoffs. First, we relate two existing fairness measures to cost-sensitive risks. Second, we show that for cost-sensitive classification and fairness measures, the optimal classifier is an instance-dependent thresholding of the class-probability function. Third, we show how the tradeoff between accuracy and fairness is determined by the…

## 14 Citations

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This work develops a general approach for solving constrained classification problems, where the loss and constraints are defined in terms of a general function of the confusion matrix, and reduces the constrained learning problem to a sequence of cost-sensitive learning tasks.

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This paper starts with a discussion of user preferences in multi-Objective optimization and then explores its relationship to fairness in machine learning and multi-objective optimization, further elaborating the importance of fairness in traditional multi- Objectives optimization, data-driven optimization and federated optimization.

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It is proved that, from a computational perspective, optimizing arbitrary objectives that take into account performance over a small number of groups is not significantly harder to optimize than average performance.

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- Computer ScienceArXiv
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It is shown that it is possible to prove that a representation function is fair according to common measures of both group and individual fairness, as well as useful with respect to a target task.

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An overview of the different schools of thought and approaches to mitigating (social) biases and increase fairness in the Machine Learning literature is provided, organises approaches into the widely accepted framework of pre-processing, in- processing, and post-processing methods, subcategorizing into a further 11 method areas.

### From Aware to Fair: Tackling Bias in A.I

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This paper will explore the nascent topic of algorithmic fairness by looking at the problem through the lens of classification tasks and foray into the concept of “fairness” and the different proposed definitions, and then compare and contrast proposed solutions.

### Towards Accuracy-Fairness Paradox: Adversarial Example-based Data Augmentation for Visual Debiasing

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To ensure the adversarial generalization as well as cross-task transferability, this paper proposes to couple the operations of target task classifier training, bias task classifiers training, and adversarial example generation to supplement the target task training dataset via balancing the distribution over bias variables in an online fashion.

### Hierarchical VampPrior Variational Fair Auto-Encoder

- Computer ScienceArXiv
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This paper proposes to use deep generative modeling and adapt a hierarchical Variational Auto-Encoder to learn fair representations that aim at removing nuisance (sensitive) information from the decision process.

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Today's legal requirements and corporate practices, while highly inconsistent across domains, offer lessons for how to approach the collection and inference of sensitive data in appropriate circumstances.

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