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On the relationship between research parasites and fairness in machine learning: challenges and opportunities

Machine learning systems influence our daily lives in many different ways. Hence, it is crucial to ensure that the decisions and recommendations made by these systems are fair, equitable, and free of unintended biases. Over the past few years, the field of fairness in machine learning has grown rapi...

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Autores principales: Nieto, Nicolás, Larrazabal, Agostina, Peterson, Victoria, Milone, Diego H, Ferrante, Enzo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8685850/
https://www.ncbi.nlm.nih.gov/pubmed/34927190
http://dx.doi.org/10.1093/gigascience/giab086
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author Nieto, Nicolás
Larrazabal, Agostina
Peterson, Victoria
Milone, Diego H
Ferrante, Enzo
author_facet Nieto, Nicolás
Larrazabal, Agostina
Peterson, Victoria
Milone, Diego H
Ferrante, Enzo
author_sort Nieto, Nicolás
collection PubMed
description Machine learning systems influence our daily lives in many different ways. Hence, it is crucial to ensure that the decisions and recommendations made by these systems are fair, equitable, and free of unintended biases. Over the past few years, the field of fairness in machine learning has grown rapidly, investigating how, when, and why these models capture, and even potentiate, biases that are deeply rooted not only in the training data but also in our society. In this Commentary, we discuss challenges and opportunities for rigorous posterior analyses of publicly available data to build fair and equitable machine learning systems, focusing on the importance of training data, model construction, and diversity in the team of developers. The thoughts presented here have grown out of the work we did, which resulted in our winning the annual Research Parasite Award that GigaSciencesponsors.
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spelling pubmed-86858502021-12-21 On the relationship between research parasites and fairness in machine learning: challenges and opportunities Nieto, Nicolás Larrazabal, Agostina Peterson, Victoria Milone, Diego H Ferrante, Enzo Gigascience Commentary Machine learning systems influence our daily lives in many different ways. Hence, it is crucial to ensure that the decisions and recommendations made by these systems are fair, equitable, and free of unintended biases. Over the past few years, the field of fairness in machine learning has grown rapidly, investigating how, when, and why these models capture, and even potentiate, biases that are deeply rooted not only in the training data but also in our society. In this Commentary, we discuss challenges and opportunities for rigorous posterior analyses of publicly available data to build fair and equitable machine learning systems, focusing on the importance of training data, model construction, and diversity in the team of developers. The thoughts presented here have grown out of the work we did, which resulted in our winning the annual Research Parasite Award that GigaSciencesponsors. Oxford University Press 2021-12-20 /pmc/articles/PMC8685850/ /pubmed/34927190 http://dx.doi.org/10.1093/gigascience/giab086 Text en © The Author(s) 2021. Published by Oxford University Press GigaScience. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Commentary
Nieto, Nicolás
Larrazabal, Agostina
Peterson, Victoria
Milone, Diego H
Ferrante, Enzo
On the relationship between research parasites and fairness in machine learning: challenges and opportunities
title On the relationship between research parasites and fairness in machine learning: challenges and opportunities
title_full On the relationship between research parasites and fairness in machine learning: challenges and opportunities
title_fullStr On the relationship between research parasites and fairness in machine learning: challenges and opportunities
title_full_unstemmed On the relationship between research parasites and fairness in machine learning: challenges and opportunities
title_short On the relationship between research parasites and fairness in machine learning: challenges and opportunities
title_sort on the relationship between research parasites and fairness in machine learning: challenges and opportunities
topic Commentary
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8685850/
https://www.ncbi.nlm.nih.gov/pubmed/34927190
http://dx.doi.org/10.1093/gigascience/giab086
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