research-article

Taking Advantage of Multitask Learning for Fair Classification

Authors:

Michele Doninini,

Amon Elders, and

Massimiliano PontilAuthors Info & Claims

AIES '19: Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society

January 2019

Pages 227 - 237

https://doi.org/10.1145/3306618.3314255

Published: 27 January 2019 Publication History

Abstract

A central goal of algorithmic fairness is to reduce bias in automated decision making. An unavoidable tension exists between accuracy gains obtained by using sensitive information as part of a statistical model, and any commitment to protect these characteristics. Often, due to biases present in the data, using the sensitive information in the functional form of a classifier improves classification accuracy. In this paper we show how it is possible to get the best of both worlds: optimize model accuracy and fairness without explicitly using the sensitive feature in the functional form of the model, thereby treating different individuals equally. Our method is based on two key ideas. On the one hand, we propose to use Multitask Learning (MTL), enhanced with fairness constraints, to jointly learn group specific classifiers that leverage information between sensitive groups. On the other hand, since learning group specific models might not be permitted, we propose to first predict the sensitive features by any learning method and then to use the predicted sensitive feature to train MTL with fairness constraints. This enables us to tackle fairness with a three-pronged approach, that is, by increasing accuracy on each group, enforcing measures of fairness during training, and protecting sensitive information during testing. Experimental results on two real datasets support our proposal, showing substantial improvements in both accuracy and fairness.

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Cited By

Caton SHaas C(2024)Fairness in Machine Learning: A SurveyACM Computing Surveys10.1145/361686556:7(1-38)Online publication date: 9-Apr-2024
https://dl.acm.org/doi/10.1145/3616865
Gupta YZhai RSuggala ARavikumar POh ANaumann TGloberson ASaenko KHardt MLevine S(2023)Responsible AI (RAI) games and ensemblesProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3669301(72717-72749)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3669301
Hort MChen ZZhang JHarman MSarro F(2023)Bias Mitigation for Machine Learning Classifiers: A Comprehensive SurveyACM Journal on Responsible Computing10.1145/36313261:2(1-52)Online publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1145/3631326
Show More Cited By

Index Terms

Taking Advantage of Multitask Learning for Fair Classification
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Multi-task learning
      2. Supervised learning
        Supervised learning by classification
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Machine learning theory
      1. Kernel methods

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cover image ACM Conferences

AIES '19: Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society

January 2019

577 pages

ISBN:9781450363242

DOI:10.1145/3306618

General Chairs:
Vincent Conitzer
Duke University, USA
,
Gillian Hadfield
University of Toronto + Vector Institute, Canada; OpenAI, USA
,
Shannon Vallor
Santa Clara University, USA

Copyright © 2019 ACM.

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SIGAI: ACM Special Interest Group on Artificial Intelligence
AAAI: American Association for Artificial Intelligence

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New York, NY, United States

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Published: 27 January 2019

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AIES '19: AAAI/ACM Conference on AI, Ethics, and Society

January 27 - 28, 2019

HI, Honolulu, USA

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Overall Acceptance Rate 61 of 162 submissions, 38%

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Cited By

Caton SHaas C(2024)Fairness in Machine Learning: A SurveyACM Computing Surveys10.1145/361686556:7(1-38)Online publication date: 9-Apr-2024
https://dl.acm.org/doi/10.1145/3616865
Gupta YZhai RSuggala ARavikumar POh ANaumann TGloberson ASaenko KHardt MLevine S(2023)Responsible AI (RAI) games and ensemblesProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3669301(72717-72749)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3669301
Hort MChen ZZhang JHarman MSarro F(2023)Bias Mitigation for Machine Learning Classifiers: A Comprehensive SurveyACM Journal on Responsible Computing10.1145/36313261:2(1-52)Online publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1145/3631326
Li TXie XWang JGuo QLiu AMa LLiu Y(2023)Faire: Repairing Fairness of Neural Networks via Neuron Condition SynthesisACM Transactions on Software Engineering and Methodology10.1145/361716833:1(1-24)Online publication date: 23-Nov-2023
https://dl.acm.org/doi/10.1145/3617168
Peng KChakraborty JMenzies T(2023)FairMask: Better Fairness via Model-Based Rebalancing of Protected AttributesIEEE Transactions on Software Engineering10.1109/TSE.2022.322071349:4(2426-2439)Online publication date: 1-Apr-2023
https://doi.org/10.1109/TSE.2022.3220713
Wu TSu HChen SHsu W(2023)Fair Robust Active Learning by Joint Inconsistency2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)10.1109/ICCVW60793.2023.00390(3624-3633)Online publication date: 2-Oct-2023
https://doi.org/10.1109/ICCVW60793.2023.00390
Li YOymak S(2023)On The Fairness of Multitask Representation LearningICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP49357.2023.10095627(1-5)Online publication date: 4-Jun-2023
https://doi.org/10.1109/ICASSP49357.2023.10095627
Yang JSoltan AEyre DYang YClifton D(2023)An adversarial training framework for mitigating algorithmic biases in clinical machine learningnpj Digital Medicine10.1038/s41746-023-00805-y6:1Online publication date: 29-Mar-2023
https://doi.org/10.1038/s41746-023-00805-y
Alkhaled LAdewumi TSabry S(2023)Bipol: A novel multi-axes bias evaluation metric with explainability for NLPNatural Language Processing Journal10.1016/j.nlp.2023.1000304(100030)Online publication date: Sep-2023
https://doi.org/10.1016/j.nlp.2023.100030
Li CDing SZou NHu XJiang XZhang K(2023)Multi-task learning with dynamic re-weighting to achieve fairness in healthcare predictive modelingJournal of Biomedical Informatics10.1016/j.jbi.2023.104399143(104399)Online publication date: Jul-2023
https://doi.org/10.1016/j.jbi.2023.104399
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