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In response to public scrutiny of data-driven algorithms, the field of data science has adopted ethics training and principles. Although ethics can help data scientists reflect on certain normative aspects of their work, such efforts are ill-equipped to generate a data science that avoids social harms and promotes social justice. In this article, I argue that data science must embrace a political orientation. Data scientists must recognize themselves as political actors engaged in normative constructions of society and evaluate their work according to its downstream impacts on people’s lives. I first articulate why data scientists must recognize themselves as political actors. In this section, I respond to three arguments that data scientists commonly invoke when challenged to take political positions regarding their work. In confronting these arguments, I describe why attempting to remain apolitical is itself a political stance—a fundamentally conservative one—and why data science’s attempts to promote “social good” dangerously rely on unarticulated and incrementalist political assumptions. I then propose a framework for how data science can evolve toward a deliberative and rigorous politics of social justice. I conceptualize the process of developing a politically engaged data science as a sequence of four stages. Pursuing these new approaches will empower data scientists with new methods for thoughtfully and rigorously contributing to social justice.


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Data Science as Political Action: Grounding Data Science in a Politics of Justice

Show Author's information Ben Green1( )
Society of Fellows and the Gerald R. Ford School of Public Policy, University of Michigan, Ann Arbor, MI 48109, USA

Abstract

In response to public scrutiny of data-driven algorithms, the field of data science has adopted ethics training and principles. Although ethics can help data scientists reflect on certain normative aspects of their work, such efforts are ill-equipped to generate a data science that avoids social harms and promotes social justice. In this article, I argue that data science must embrace a political orientation. Data scientists must recognize themselves as political actors engaged in normative constructions of society and evaluate their work according to its downstream impacts on people’s lives. I first articulate why data scientists must recognize themselves as political actors. In this section, I respond to three arguments that data scientists commonly invoke when challenged to take political positions regarding their work. In confronting these arguments, I describe why attempting to remain apolitical is itself a political stance—a fundamentally conservative one—and why data science’s attempts to promote “social good” dangerously rely on unarticulated and incrementalist political assumptions. I then propose a framework for how data science can evolve toward a deliberative and rigorous politics of social justice. I conceptualize the process of developing a politically engaged data science as a sequence of four stages. Pursuing these new approaches will empower data scientists with new methods for thoughtfully and rigorously contributing to social justice.

Keywords:

data science, ethics, politics, social justice, social change, social good, pedagogy
Received: 20 May 2021 Revised: 19 November 2021 Accepted: 25 November 2021 Published: 13 January 2022 Issue date: September 2021
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Publication history
Copyright
Acknowledgements
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Publication history

Received: 20 May 2021
Revised: 19 November 2021
Accepted: 25 November 2021
Published: 13 January 2022
Issue date: September 2021

Copyright

© The author(s) 2021

Acknowledgements

Acknowledgment

B. Green is grateful to the Berkman Klein Center Ethical Tech Working Group for fostering his thinking on matters of technology, ethics, and politics. B. Green also thanks Catherine D’Ignazio, Anna Lauren Hoffmann, Lily Hu, Momin Malik, Dan McQuillan, Luke Stark, Salomé Viljoen, and the reviewers for providing helpful discussions and suggestions.

Rights and permissions

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

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