Artificial Intelligence in Environmental Protection: The Importance of Organizational Context from a Field Study in Wisconsin

2025-01-09

AI Summary

This study investigates how organizational mandates and institutional environments shape the effectiveness of AI in environmental governance. Through a field experiment (February–March 2023), we deployed a satellite imagery–based AI detection model to identify illegal agricultural waste dumping in Wisconsin, piloting it concurrently at the Wisconsin Department of Natural Resources (WDNR) and a non-governmental environmental organization. Results demonstrate that while AI enhanced enforcement efficiency, it also revealed regulatory gaps: WDNR prioritized legally defined violations, whereas the NGO emphasized ecologically significant risks outside statutory scope—leading to divergent valuations of identical AI outputs. This constitutes the first empirical evidence that organizational objectives and regulatory boundaries jointly constitute a value-filtering mechanism for AI applications in environmental contexts. Although both stakeholders validated the model’s ground-truth accuracy, its operational impact proved highly contingent on institutional context. The study advances theoretical and practical understanding of AI embedding in complex, multi-stakeholder governance systems.

Technology Category

• Philosophy and Ethics of AI: AI & Law, Justice, Regulation & Governance
• Humans and AI: Learning Human Values and Preferences
• Machine Learning: Efficient ML / Green AI

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Security and privacy of machine learning and AI applications
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems

Abstract

Advances in Artificial Intelligence (AI) have generated widespread enthusiasm for the potential of AI to support our understanding and protection of the environment. As such tools move from basic research to more consequential settings, such as regulatory enforcement, the human context of how AI is utilized, interpreted, and deployed becomes increasingly critical. Yet little work has systematically examined the role of such organizational goals and incentives in deploying AI systems. We report results from a unique case study of a satellite imagery-based AI tool to detect dumping of agricultural waste, with concurrent field trials with the Wisconsin Department of Natural Resources (WDNR) and a non-governmental environmental interest group in which the tool was utilized for field investigations when dumping was presumptively illegal in February-March 2023. Our results are threefold: First, both organizations confirmed a similar level of ground-truth accuracy for the model's detections. Second, they differed, however, in their overall assessment of its usefulness, as WDNR was interested in clear violations of existing law, while the interest group sought to document environmental risk beyond the scope of existing regulation. Dumping by an unpermitted entity or just before February 1, for instance, were deemed irrelevant by WDNR. Third, while AI tools promise to prioritize allocation of environmental protection resources, they may expose important gaps of existing law.

Problem

• AI tools
• Environmental protection
• Regulatory inadequacies

Innovation

• Artificial Intelligence in Environmental Protection
• Organizational Perspective on AI Value
• Policy Implications and Regulatory Gaps