Algorithmic reparation

Machine learning algorithms pervade contemporary society. They are integral to social institutions, inform processes of governance, and animate the mundane technologies of daily life. Consistently, the outcomes of machine learning reflect, reproduce, and amplify structural inequalities. The field of...

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Autores principales:	Jenny L. Davis, Apryl Williams, Michael W. Yang
Formato:	article
Lenguaje:	EN
Publicado:	SAGE Publishing 2021
Materias:	General Works A
Acceso en línea:	https://doaj.org/article/7efb5c773c854102a81ace44c1b199e2
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id	oai:doaj.org-article:7efb5c773c854102a81ace44c1b199e2
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spelling	oai:doaj.org-article:7efb5c773c854102a81ace44c1b199e22021-11-13T14:03:19ZAlgorithmic reparation2053-951710.1177/20539517211044808https://doaj.org/article/7efb5c773c854102a81ace44c1b199e22021-07-01T00:00:00Zhttps://doi.org/10.1177/20539517211044808https://doaj.org/toc/2053-9517Machine learning algorithms pervade contemporary society. They are integral to social institutions, inform processes of governance, and animate the mundane technologies of daily life. Consistently, the outcomes of machine learning reflect, reproduce, and amplify structural inequalities. The field of fair machine learning has emerged in response, developing mathematical techniques that increase fairness based on anti-classification, classification parity, and calibration standards. In practice, these computational correctives invariably fall short, operating from an algorithmic idealism that does not, and cannot, address systemic, Intersectional stratifications. Taking present fair machine learning methods as our point of departure, we suggest instead the notion and practice of algorithmic reparation. Rooted in theories of Intersectionality, reparative algorithms name, unmask, and undo allocative and representational harms as they materialize in sociotechnical form. We propose algorithmic reparation as a foundation for building, evaluating, adjusting, and when necessary, omitting and eradicating machine learning systems.Jenny L. DavisApryl WilliamsMichael W. YangSAGE PublishingarticleGeneral WorksAENBig Data & Society, Vol 8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	General Works A
spellingShingle	General Works A Jenny L. Davis Apryl Williams Michael W. Yang Algorithmic reparation
description	Machine learning algorithms pervade contemporary society. They are integral to social institutions, inform processes of governance, and animate the mundane technologies of daily life. Consistently, the outcomes of machine learning reflect, reproduce, and amplify structural inequalities. The field of fair machine learning has emerged in response, developing mathematical techniques that increase fairness based on anti-classification, classification parity, and calibration standards. In practice, these computational correctives invariably fall short, operating from an algorithmic idealism that does not, and cannot, address systemic, Intersectional stratifications. Taking present fair machine learning methods as our point of departure, we suggest instead the notion and practice of algorithmic reparation. Rooted in theories of Intersectionality, reparative algorithms name, unmask, and undo allocative and representational harms as they materialize in sociotechnical form. We propose algorithmic reparation as a foundation for building, evaluating, adjusting, and when necessary, omitting and eradicating machine learning systems.
format	article
author	Jenny L. Davis Apryl Williams Michael W. Yang
author_facet	Jenny L. Davis Apryl Williams Michael W. Yang
author_sort	Jenny L. Davis
title	Algorithmic reparation
title_short	Algorithmic reparation
title_full	Algorithmic reparation
title_fullStr	Algorithmic reparation
title_full_unstemmed	Algorithmic reparation
title_sort	algorithmic reparation
publisher	SAGE Publishing
publishDate	2021
url	https://doaj.org/article/7efb5c773c854102a81ace44c1b199e2
work_keys_str_mv	AT jennyldavis algorithmicreparation AT aprylwilliams algorithmicreparation AT michaelwyang algorithmicreparation
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Algorithmic reparation

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