AI-Driven Environmental Impact Assessments: Ethical and Legal Considerations

Abstract

Integrating artificial intelligence (AI) into Environmental Impact Assessments (EIAs) could potentially enhance the efficiency, accuracy, and transparency of the process. That said, there are still concerns about potential biases, following the legal standards, and transparent decision-making procedures. This study evaluates the performance of AI-driven EIAs (electronic information applications) with respect to accuracy, transparency, bias detection, and regulatory compliance. It also aims to identify areas for potential development and lead to recommendations for better aligning AI technologies with existing legal systems. A mixed methods study design included qualitative stakeholder interviews and quantitative analysis. We compared the performance of the AI model with existing benchmarks using statistical impurities, such as entropy-based transparency metrics, bias detecting measures, and the Clopper-Pearson confidence interval. Efficiency, accuracy, and compliance were used to evaluate the solution's respective performance. The AI based approach used in EIA model showed substantial gains with (8.2%) increase in accuracy, (60%) decrease in manpower requirement and (40%) decrease in operational cost. Transparency measures reported much higher reporting rates, and bias detection had lower false positive and false negative rates. It has proven adherent to compliance, within a tight confidence interval range) which means it can be forensically relied upon and defended in a court of law. The prospects for using AI in Environmental Impact Assessments are extensive and could lead to more reliable, efficient, and transparent systems that can significantly improve environmental compliance. These findings could help set the stage for additional research to refine AI practices and develop standardized legal frameworks capable of ensuring fairness and accountability within environmental decision-making processes.