Automated Decision-Making and Environmental Impact Assessments: Decisions, Data Analysis and Predictions

This article critically examines the opportunities and challenges that automated decision-making (ADM) poses for environmental impact assessments (EIAs) as a crucial aspect of environmental law. It argues that while fully or partially automating discretionary EIA decisions is legally and technically...

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Autores principales:	Zoe Nay, Anna Huggins, Felicity Deane
Formato:	article
Lenguaje:	EN
Publicado:	Queensland University of Technology 2021
Materias:	environmental impact assessments automated decision-making discretionary decisions data-driven decision-making Law in general. Comparative and uniform law. Jurisprudence K1-7720
Acceso en línea:	https://doaj.org/article/56cd44c6afe34491be12e55303375b8a
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spelling	oai:doaj.org-article:56cd44c6afe34491be12e55303375b8a2021-11-08T01:48:43ZAutomated Decision-Making and Environmental Impact Assessments: Decisions, Data Analysis and Predictions2652-407410.5204/lthj.1846https://doaj.org/article/56cd44c6afe34491be12e55303375b8a2021-11-01T00:00:00Zhttps://lthj.qut.edu.au/article/view/1846https://doaj.org/toc/2652-4074This article critically examines the opportunities and challenges that automated decision-making (ADM) poses for environmental impact assessments (EIAs) as a crucial aspect of environmental law. It argues that while fully or partially automating discretionary EIA decisions is legally and technically problematic, there is significant potential for data-driven decision-making tools to provide superior analysis and predictions to better inform EIA processes. Discretionary decision-making is desirable for EIA decisions given the inherent complexity associated with environmental regulation and the prediction of future impacts. This article demonstrates that current ADM tools cannot adequately replicate human discretionary processes for EIAs—even if there is human oversight and review of automated outputs. Instead of fully or partially automating EIA decisions, data-driven decision-making can be more appropriately deployed to enhance data analysis and predictions to optimise EIA decision-making processes. This latter type of ADM can augment decision-making processes without displacing the critical role of human discretion in weighing the complex environmental, social and economic considerations inherent in EIA determinations.Zoe NayAnna HugginsFelicity DeaneQueensland University of Technologyarticleenvironmental impact assessmentsautomated decision-makingdiscretionary decisionsdata-driven decision-makingLaw in general. Comparative and uniform law. JurisprudenceK1-7720ENLaw, Technology and Humans, Vol 3, Iss 2, Pp 76-90 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	environmental impact assessments automated decision-making discretionary decisions data-driven decision-making Law in general. Comparative and uniform law. Jurisprudence K1-7720
spellingShingle	environmental impact assessments automated decision-making discretionary decisions data-driven decision-making Law in general. Comparative and uniform law. Jurisprudence K1-7720 Zoe Nay Anna Huggins Felicity Deane Automated Decision-Making and Environmental Impact Assessments: Decisions, Data Analysis and Predictions
description	This article critically examines the opportunities and challenges that automated decision-making (ADM) poses for environmental impact assessments (EIAs) as a crucial aspect of environmental law. It argues that while fully or partially automating discretionary EIA decisions is legally and technically problematic, there is significant potential for data-driven decision-making tools to provide superior analysis and predictions to better inform EIA processes. Discretionary decision-making is desirable for EIA decisions given the inherent complexity associated with environmental regulation and the prediction of future impacts. This article demonstrates that current ADM tools cannot adequately replicate human discretionary processes for EIAs—even if there is human oversight and review of automated outputs. Instead of fully or partially automating EIA decisions, data-driven decision-making can be more appropriately deployed to enhance data analysis and predictions to optimise EIA decision-making processes. This latter type of ADM can augment decision-making processes without displacing the critical role of human discretion in weighing the complex environmental, social and economic considerations inherent in EIA determinations.
format	article
author	Zoe Nay Anna Huggins Felicity Deane
author_facet	Zoe Nay Anna Huggins Felicity Deane
author_sort	Zoe Nay
title	Automated Decision-Making and Environmental Impact Assessments: Decisions, Data Analysis and Predictions
title_short	Automated Decision-Making and Environmental Impact Assessments: Decisions, Data Analysis and Predictions
title_full	Automated Decision-Making and Environmental Impact Assessments: Decisions, Data Analysis and Predictions
title_fullStr	Automated Decision-Making and Environmental Impact Assessments: Decisions, Data Analysis and Predictions
title_full_unstemmed	Automated Decision-Making and Environmental Impact Assessments: Decisions, Data Analysis and Predictions
title_sort	automated decision-making and environmental impact assessments: decisions, data analysis and predictions
publisher	Queensland University of Technology
publishDate	2021
url	https://doaj.org/article/56cd44c6afe34491be12e55303375b8a
work_keys_str_mv	AT zoenay automateddecisionmakingandenvironmentalimpactassessmentsdecisionsdataanalysisandpredictions AT annahuggins automateddecisionmakingandenvironmentalimpactassessmentsdecisionsdataanalysisandpredictions AT felicitydeane automateddecisionmakingandenvironmentalimpactassessmentsdecisionsdataanalysisandpredictions
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Automated Decision-Making and Environmental Impact Assessments: Decisions, Data Analysis and Predictions

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