# Fair classification and social welfare

@article{Hu2019FairCA, title={Fair classification and social welfare}, author={Lily Hu and Yiling Chen}, journal={Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency}, year={2019} }

Now that machine learning algorithms lie at the center of many important resource allocation pipelines, computer scientists have been unwittingly cast as partial social planners. Given this state of affairs, important questions follow. How do leading notions of fairness as defined by computer scientists map onto longer-standing notions of social welfare? In this paper, we present a welfare-based analysis of fair classification regimes. Our main findings assess the welfare impact of fairness…

## 63 Citations

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A novel methodology is presented to explore the tradeoff in terms of a Pareto front between accuracy and fairness, and proposes a multiobjective framework that seeks to optimize both measures.

### Balancing Competing Objectives with Noisy Data: Score-Based Classifiers for Welfare-Aware Machine Learning

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The theoretical results characterize the optimal strategies in this class of policies, bound the Pareto errors due to inaccuracies in the scores, and show an equivalence between optimal strategies and a rich class of fairness-constrained profit-maximizing policies.

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It is shown how optimization models can assist fairness-oriented decision making in the context of neural networks, support vector machines, and rule-based systems by maximizing a social welfare function subject to appropriate constraints.

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This work proposes to generate rankings by maximizing concave welfare functions, and develops an efficient inference procedure based on the Frank-Wolfe algorithm that guarantees that rankings are Pareto efficient, and that they maximally redistribute utility from better-off to worse-off, at a given level of overall utility.

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The proposed framework provides a suitable solution to address group fairness concerns in the development of scoring systems and enables policymakers to set and customize their desired fairness requirements as well as other application-specific constraints.

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