AI-Assisted Regulatory Compliance Framework for Animal Welfare and Environmental Protection Laws

Abstract

Ensuring compliance with animal welfare and environmental protection laws is essential for sustainable livestock production, biodiversity conservation, and responsible environmental management. However, conventional compliance practices rely heavily on manual inspections, periodic audits, and extensive documentation, making them time-consuming, costly, and prone to human error. This study proposes an AI-Assisted Regulatory Compliance Framework that integrates Artificial Intelligence (AI), Machine Learning (ML), Computer Vision (CV), Natural Language Processing (NLP), Internet of Things (IoT), and Explainable Artificial Intelligence (XAI) to automate regulatory compliance assessment. The framework collects multimodal data from regulatory documents, environmental sensors, surveillance systems, wearable animal devices, veterinary records, and farm management platforms. NLP extracts legal obligations from regulatory documents, while Computer Vision and ML analyze animal welfare conditions and environmental parameters to detect potential compliance violations. An Explainable AI module provides transparent justifications and recommends corrective actions, enabling evidence-based regulatory decision-making. The proposed framework is validated through representative use-case scenarios involving animal welfare monitoring, environmental protection, waste management, and legal compliance assessment. The results demonstrate the framework's ability to improve compliance accuracy, reduce assessment time, support continuous monitoring, and facilitate proactive regulatory enforcement. By integrating intelligent analytics with legal knowledge, the proposed framework offers a scalable and transparent solution for enhancing regulatory compliance, improving animal welfare, promoting environmental sustainability, and supporting informed governance across diverse agricultural and environmental management systems.