research-article

Context-conscious fairness in using machine learning to make decisions

Author:

Michelle Seng Ah LeeAuthors Info & Claims

AI Matters, Volume 5, Issue 2

Pages 23 - 29

https://doi.org/10.1145/3340470.3340477

Published: 05 August 2019 Publication History

Abstract

The increasing adoption of machine learning to inform decisions in employment, pricing, and criminal justice has raised concerns that algorithms may perpetuate historical and societal discrimination. Academics have responded by introducing numerous definitions of "fairness" with corresponding mathematical formalisations, proposed as one-size-fits-all, universal conditions. This paper will explore three of the definitions and demonstrate their embedded ethical values and contextual limitations, using credit risk evaluation as an example use case. I will propose a new approach - context-conscious fairness - that takes into account two main trade-offs: between aggregate benefit and inequity and between accuracy and interpretability. Fairness is not a notion with absolute and binary measurement; the target outcomes and their trade-offs must be specified with respect to the relevant domain context.

References

[1]

Baum, J. S. J. J., D., & Stute, D. (2015). Supreme court affirms fha disparate impact claims.

[2]

Bertrand, M., & Mullainathan, S. (2003, July). Are emily and greg more employable than lakisha and jamal? a field experiment on labor market discrimination (Working Paper No. 9873). National Bureau of Economic Research. Retrieved from http://www.nber.org/papers/w9873

[3]

Deku, S. Y., Kara, A., & Molyneux, P. (2016). Access to consumer credit in the uk. The European Journal of Finance, 22(10), 941--964. Retrieved from

[4]

Dwork, C., Hardt, M., Pitassi, T., Reingold, O., & Zemel, R. S. (2011). Fairness through awareness. CoRR, abs/1104.3913. Retrieved from http://arxiv.org/abs/1104.3913

[5]

Dworkin, R. (1981). What is equality? part 1: Equality of welfare. Philosophy and Public Affairs, 10(3), 185--246.

[6]

European Commission AI HLEG. (2019). Draft ai ethics guidelines for trustworthy ai (Tech. Rep.). European Commission High-Level Expert Group on Artificial Intelligence (AI HLEG). Retrieved from https://ec.europa.eu/futurium/en/node/6044

[7]

Fuster, A., Goldsmith-Pinkham, P., Ramadorai, T., & Walther, A. (2017, 01). Predictably unequal? the effects of machine learning on credit markets. SSRN Electronic Journal.

[8]

Gajane, P. (2017). On formalizing fairness in prediction with machine learning. CoRR, abs/1710.03184. Retrieved from http://arxiv.org/abs/1710.03184

[9]

Grosz, R. A. E. H. A. M. T. M. D. M. Y. S., Barbara. (2015). Artificial intelligence and life in 2030 (Tech. Rep.). Stanford University. Retrieved from https://ai100.stanford.edu/sites/default/files/ai_100_report_0831fnl.pdf

[10]

Hacker, P., & Wiedemann, E. (2017). A continuous framework for fairness. CoRR, abs/1712.07924. Retrieved from http://arxiv.org/abs/1712.07924

[11]

Holden, J., & Smith, M. (2016). Preparing for the future of ai (Tech. Rep.). U.S. Executive Office of the President, National Science and Technology Council Committee on Technology. Retrieved from https://obamawhitehouse.archives.gov/sites/default/files/whitehouse_files/microsites/ostp/NSTC/preparing_for_the_future_of_ai.pdf

[12]

IEEE Global Initiative. (2018). Ethically aligned design: A vision for prioritizing human well-being with autonomous and intelligent systems. Retrieved from https://standards.ieee.org/content/dam/ieee-standards/standards/web/documents/other/ead_v2.pdf

[13]

Kendig, D. (1973). Discrimination against women in home mortgage financing. Yale Review of Law and Social Action, 3(2).

[14]

Koren, J. R. (2016, Jul). What does that web search say about your credit? Retrieved from https://www.latimes.com/business/la-fi-zestfinance-baidu-20160715-snap-story.html

[15]

Kusner, M. J., Loftus, J. R., Russell, C., & Silva, R. (2017, March). Counterfactual Fairness. arXiv e-prints, arXiv:1703.06856.

[16]

Lewis, D. (1973). Causation. Journal of Philosophy, 70(17), 556--567.

[17]

Lowenthal, T. (2017). Essop v home office: Proving indirect discrimination.

[18]

Nelson, R. K., Winling, L., Marciano, R., & Connolly, N. (2016). Mapping inequality. American Panorama. Retrieved from https://dsl.richmond.edu/panorama/redlining/#loc=5/36.721/-96.943&opacity=0.8&text=intro

[19]

Rawls, J. (1971). A theory of justice. Harvard University Press.

Cited By

McCormack LBendechache M(2024)A comprehensive survey and classification of evaluation criteria for trustworthy artificial intelligenceAI and Ethics10.1007/s43681-024-00590-8Online publication date: 21-Oct-2024
https://doi.org/10.1007/s43681-024-00590-8
Zheng YWang QZhuang DWang SZhao J(2023)Fairness-Enhancing Deep Learning for Ride-Hailing Demand PredictionIEEE Open Journal of Intelligent Transportation Systems10.1109/OJITS.2023.32975174(551-569)Online publication date: 2023
https://doi.org/10.1109/OJITS.2023.3297517
Klumbytė GDraude CTaylor A(2022)Critical Tools for Machine Learning: Working with Intersectional Critical Concepts in Machine Learning Systems DesignProceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency10.1145/3531146.3533207(1528-1541)Online publication date: 21-Jun-2022
https://dl.acm.org/doi/10.1145/3531146.3533207
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Published In

cover image AI Matters

AI Matters Volume 5, Issue 2

June 2019

44 pages

EISSN:2372-3483

DOI:10.1145/3340470

Editors:
Amy McGovern
U. Oklahoma
,
Iolanda Leite
KTH

Issue’s Table of Contents

Copyright © 2019 Author.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 August 2019

Published in SIGAI-AIMATTERS Volume 5, Issue 2

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Cited By

McCormack LBendechache M(2024)A comprehensive survey and classification of evaluation criteria for trustworthy artificial intelligenceAI and Ethics10.1007/s43681-024-00590-8Online publication date: 21-Oct-2024
https://doi.org/10.1007/s43681-024-00590-8
Zheng YWang QZhuang DWang SZhao J(2023)Fairness-Enhancing Deep Learning for Ride-Hailing Demand PredictionIEEE Open Journal of Intelligent Transportation Systems10.1109/OJITS.2023.32975174(551-569)Online publication date: 2023
https://doi.org/10.1109/OJITS.2023.3297517
Klumbytė GDraude CTaylor A(2022)Critical Tools for Machine Learning: Working with Intersectional Critical Concepts in Machine Learning Systems DesignProceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency10.1145/3531146.3533207(1528-1541)Online publication date: 21-Jun-2022
https://dl.acm.org/doi/10.1145/3531146.3533207
Zheng YWang SZhao J(2021)Equality of opportunity in travel behavior prediction with deep neural networks and discrete choice modelsTransportation Research Part C: Emerging Technologies10.1016/j.trc.2021.103410132(103410)Online publication date: Nov-2021
https://doi.org/10.1016/j.trc.2021.103410
Lee MFloridi L(2021)Algorithmic Fairness in Mortgage Lending: from Absolute Conditions to Relational Trade-offsMinds and Machines10.1007/s11023-020-09529-431:1(165-191)Online publication date: 1-Mar-2021
https://dl.acm.org/doi/10.1007/s11023-020-09529-4
Lee MFloridi L(2021)Algorithmic Fairness in Mortgage Lending: From Absolute Conditions to Relational Trade-offsThe 2020 Yearbook of the Digital Ethics Lab10.1007/978-3-030-80083-3_12(145-171)Online publication date: 31-Oct-2021
https://doi.org/10.1007/978-3-030-80083-3_12
Lee MFloridi L(undefined)Algorithmic Fairness in Mortgage Lending: From Absolute Conditions to Relational Trade-OffsSSRN Electronic Journal10.2139/ssrn.3559407
https://doi.org/10.2139/ssrn.3559407

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