Automated decision-making and environmental impact assessments: Decisions, data analysis and predictions

• Published in: Law, technology and humans Vol. 3; no. 2; pp. 76 - 90
• Main Authors: Zoe Nay, Anna Huggins, Felicity Deane
• Format: Journal Article
• Published: Brisbane Faculty of Law, Queensland University of Techonlogy 01.11.2021
• Subjects: Algorithms; Decision making; Ecology; Environmental aspects; Environmental impact analysis; Environmental law; Evaluation; Methodology; Sustainable development
• ISSN: 2652-4074; 2652-4074

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.