Open AccessAutomated Decision-Making and Environmental Impact Assessments: Decisions, Data Analysis and Predictions
Author(s) -
Zoe Nay,
Anna Huggins,
Felicity Deane
Publication year - 2021
Publication title -
law, technology and humans
Language(s) - English
Resource type - Journals
ISSN - 2652-4074
DOI - 10.5204/lthj.1846
Subject(s) - replicate , discretion , computer science , risk analysis (engineering) , environmental impact assessment , decision analysis , decision support system , management science , operations research , business , engineering , artificial intelligence , economics , political science , law , statistics , mathematics , mathematical economics
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.