# Lexicographically Fair Learning: Algorithms and Generalization

@inproceedings{Diana2021LexicographicallyFL, title={Lexicographically Fair Learning: Algorithms and Generalization}, author={Emily Diana and Wesley Gill and Ira Globus-Harris and Michael Kearns and Aaron Roth and Saeed Sharifi-Malvajerdi}, booktitle={FORC}, year={2021} }

We extend the notion of minimax fairness in supervised learning problems to its natural conclusion: lexicographic minimax fairness (or lexifairness for short). Informally, given a collection of demographic groups of interest, minimax fairness asks that the error of the group with the highest error be minimized. Lexifairness goes further and asks that amongst all minimax fair solutions, the error of the group with the second highest error should be minimized, and amongst all of those solutions…

## 4 Citations

Beyond the Frontier: Fairness Without Accuracy Loss

- Computer ScienceArXiv
- 2022

A simple algorithmic framework that allows us to deploy models and then revise them dynamically when groups are discovered on which the error rate is suboptimal is developed, and the result is provably fast convergence to a model that cannot be distinguished from the Bayes optimal predictor — at least by the party tasked with finding high error groups.

Multiaccurate Proxies for Downstream Fairness

- Computer ScienceArXiv
- 2021

This work adopts a fairness pipeline perspective, and shows that obeying multiaccuracy constraints with respect to the downstream model class suffices for this purpose, and provides sampleand oracle efficient-algorithms and generalization bounds for learning such proxies.

Achieving Downstream Fairness with Geometric Repair

- Computer ScienceArXiv
- 2022

This work presents a technique that specifically addresses the setting where a protected attribute takes on multiple values, by post-processing a regressor’s scores such they yield fair classifications for any downstream choice in decision threshold.

An Algorithmic Framework for Bias Bounties

- Computer Science
- 2022

An algorithmic framework for "bias bounties" — events in which external participants are invited to propose improvements to a trained model, akin to bug bounty events in software and security, which allows participants to submit arbitrary subgroup improvements, which are algorithmically incorporated into an updated model.

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