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Embed systemic equity throughout industrial ecology applications: How to address machine learning unfairness and bias

Lookup NU author(s): Dr Shalini NakkasunchiORCiD, Professor Darren McCauleyORCiD, Professor Oliver Heidrich

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).


Abstract

© 2024 The Authors. Journal of Industrial Ecology published by Wiley Periodicals LLC on behalf of International Society for Industrial Ecology. Recent calls have been made for equity tools and frameworks to be integrated throughout the research and design life cycle —from conception to implementation—with an emphasis on reducing inequity in artificial intelligence (AI) and machine learning (ML) applications. Simply stating that equity should be integrated throughout, however, leaves much to be desired as industrial ecology (IE) researchers, practitioners, and decision-makers attempt to employ equitable practices. In this forum piece, we use a critical review approach to explain how socioecological inequities emerge in ML applications across their life cycle stages by leveraging the food system. We exemplify the use of a comprehensive questionnaire to delineate unfair ML bias across data bias, algorithmic bias, and selection and deployment bias categories. Finally, we provide consolidated guidance and tailored strategies to help address AI/ML unfair bias and inequity in IE applications. Specifically, the guidance and tools help to address sensitivity, reliability, and uncertainty challenges. There is also discussion on how bias and inequity in AI/ML affect other IE research and design domains, besides the food system—such as living labs and circularity. We conclude with an explanation of the future directions IE should take to address unfair bias and inequity in AI/ML. Last, we call for systemic equity to be embedded throughout IE applications to fundamentally understand domain-specific socioecological inequities, identify potential unfairness in ML, and select mitigation strategies in a manner that translates across different research domains.


Publication metadata

Author(s): Bozeman JF, Hollauer C, Ramshankar AT, Nakkasunchi S, Jambeck J, Hicks A, Bilec M, McCauley D, Heidrich O

Publication type: Article

Publication status: Published

Journal: Journal of Industrial Ecology

Year: 2024

Pages: ePub ahead of Print

Online publication date: 18/06/2024

Acceptance date: 02/04/2018

Date deposited: 01/07/2024

ISSN (print): 1088-1980

ISSN (electronic): 1530-9290

Publisher: John Wiley and Sons Inc.

URL: https://doi.org/10.1111/jiec.13509

DOI: 10.1111/jiec.13509

Data Access Statement: Data sharing is not applicable—no new data were generated.


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Funding

Funder referenceFunder name
Georgia Institute of Technology's Renewable Bioproduct Institute, the National Science Foundation (Grant number: 2236080)
United Kingdom Ministry of Defence's Defence Innovation Fund Top-Level Budget Ideas Scheme (61182036)

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