# Fair classification and social welfare

@article{Hu2020FairCA, title={Fair classification and social welfare}, author={Lily Hu and Yiling Chen}, journal={Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency}, year={2020} }

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…

## 36 Citations

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

- Computer Science, MathematicsICML
- 2020

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.

Protecting the Protected Group: Circumventing Harmful Fairness

- Computer ScienceAAAI
- 2021

The welfare-Equalizing approach provides a unified framework for discussing fairness in classification in the presence of a self-interested party and finds that the disadvantaged protected group can be worse off after imposing a fairness constraint.

An Axiomatic Theory of Provably-Fair Welfare-Centric Machine Learning

- Computer ScienceArXiv
- 2021

We address an inherent difficulty in welfare-theoretic fair ML, by proposing an equivalently-axiomatically justified alternative setting, and studying the resulting computational and statistical…

Fairness in Machine Learning

- Computer Science, MathematicsArXiv
- 2020

It is shown how causal Bayesian networks can play an important role to reason about and deal with fairness, especially in complex unfairness scenarios, and how optimal transport theory can be leveraged to develop methods that impose constraints on the full shapes of distributions corresponding to different sensitive attributes.

Two-sided fairness in rankings via Lorenz dominance

- Computer ScienceArXiv
- 2021

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.

Welfare-based Fairness through Optimization

- Computer Science, Mathematics
- 2021

It is argued that optimization models allow formulation of a wide range of fairness criteria as social welfare functions, while enabling AI to take advantage of highly advanced solution technology, and supports a broad perspective on fairness motivated by general distributive justice considerations.

Novel Concentration of Measure Bounds with Applications to Fairness in Machine Learning

- 2020

I introduce novel concentration-of-measure bounds for the supremum deviation, several variance concepts, and a family of game-theoretic welfare functions. In particular, I introduce empirically…

Algorithmic and Economic Perspectives on Fairness

- Computer ScienceArXiv
- 2019

Algorithmic systems used to inform consequential decisions in medicine, medicine, criminal justice, facial recognition, lending and insurance, and the allocation of public services, are deployed to screen job applicants for the recommendation of products, people, and content.

Fairness On The Ground: Applying Algorithmic Fairness Approaches to Production Systems

- Computer ScienceArXiv
- 2021

It is hoped the experience integrating fairness tools and approaches into large-scale and complex production systems will be useful to other practitioners facing similar challenges, and illuminating to academics and researchers looking to better address the needs of practitioners.

Fairness without Harm: Decoupled Classifiers with Preference Guarantees

- Computer ScienceICML
- 2019

It is argued that when there is this kind of treatment disparity then it should be in the best interest of each group, and a recursive procedure is introduced that adaptively selects group attributes for decoupling to ensure preference guarantees in terms of generalization error.

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