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Improving fairness in machine learning-enabled affirmative actions: a case study in outreach activities in healthcare

Improving fairness in machine learning-enabled affirmative actions: a case study in outreach activities in healthcare
Improving fairness in machine learning-enabled affirmative actions: a case study in outreach activities in healthcare

Over the last decade, due to the growing availability of data and computational resources, machine learning (ML) approaches have started to play a key role in the implementation of affirmative-action policies and programs. The underlying assumption is that resource allocation can be informed by the prediction of individual risks, improving the prioritization of the potential beneficiaries, and increasing the performance of the system. Therefore, it is important to ensure that biases in the data or the algorithms do not lead to treating some individuals unfavourably. In particular, the notion of group-based fairness seeks to ensure that individuals will not be discriminated against on the basis of their group’s protected characteristics. This work proposes an optimization model to improve fairness in ML-enabled affirmative actions, following a post-processing approach. Our case study is an outreach program to increase cervical cancer screening among hard-to-reach women in Bogotá, Colombia. Bias may occur since the protected group (women in the most severe poverty) are under-represented in the data. Computational experiments show that it is possible to address ML bias while maintaining high levels of accuracy.

Machine learning, fairness, preventive healthcare programs
0160-5682
Barrera Ferro, David
fd5d8392-4ffd-4982-867a-ef38c0ef05eb
Brailsford, Sally
634585ff-c828-46ca-b33d-7ac017dda04f
Chapman, Adriane
721b7321-8904-4be2-9b01-876c430743f1
Barrera Ferro, David
fd5d8392-4ffd-4982-867a-ef38c0ef05eb
Brailsford, Sally
634585ff-c828-46ca-b33d-7ac017dda04f
Chapman, Adriane
721b7321-8904-4be2-9b01-876c430743f1

Barrera Ferro, David, Brailsford, Sally and Chapman, Adriane (2024) Improving fairness in machine learning-enabled affirmative actions: a case study in outreach activities in healthcare. Journal of the Operational Research Society. (doi:10.1080/01605682.2024.2354364).

Record type: Article

Abstract

Over the last decade, due to the growing availability of data and computational resources, machine learning (ML) approaches have started to play a key role in the implementation of affirmative-action policies and programs. The underlying assumption is that resource allocation can be informed by the prediction of individual risks, improving the prioritization of the potential beneficiaries, and increasing the performance of the system. Therefore, it is important to ensure that biases in the data or the algorithms do not lead to treating some individuals unfavourably. In particular, the notion of group-based fairness seeks to ensure that individuals will not be discriminated against on the basis of their group’s protected characteristics. This work proposes an optimization model to improve fairness in ML-enabled affirmative actions, following a post-processing approach. Our case study is an outreach program to increase cervical cancer screening among hard-to-reach women in Bogotá, Colombia. Bias may occur since the protected group (women in the most severe poverty) are under-represented in the data. Computational experiments show that it is possible to address ML bias while maintaining high levels of accuracy.

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Barrera Ferro et al - Accepted Manuscript
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Accepted/In Press date: 8 May 2024
e-pub ahead of print date: 24 May 2024
Keywords: Machine learning, fairness, preventive healthcare programs

Identifiers

Local EPrints ID: 492756
URI: http://eprints.soton.ac.uk/id/eprint/492756
ISSN: 0160-5682
PURE UUID: 2905ef04-c6b7-4bc8-b71e-50e05f3dd564
ORCID for Sally Brailsford: ORCID iD orcid.org/0000-0002-6665-8230
ORCID for Adriane Chapman: ORCID iD orcid.org/0000-0002-3814-2587

Catalogue record

Date deposited: 13 Aug 2024 16:55
Last modified: 14 Aug 2024 01:52

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Contributors

Author: David Barrera Ferro
Author: Adriane Chapman ORCID iD

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