Machine learning and sustainable green forensics

Abstract

As digital evidence continues to grow at an alarming rate, the use of machine learning (ML) in digital forensics presents an unparalleled opportunity to improve the efficiency of investigations while lowering environmental and ethical costs. This chapter examines the developing concept of sustainable green forensics, which utilizes computational intelligence to enhance forensic procedures, reduce energy usage, and maintain ethical and legal standards. Forensic investigators can make decisions faster without putting data integrity or privacy at risk by using energy-efficient algorithms, automated evidence triaging, and adaptive ML models. The chapter goes on to look at how ML can help with both operational sustainability and ethical accountability. Edge-based forensic analysis, federated learning, and carbon-aware computing architectures are some of the methods that could help lower the carbon footprint of large-scale digital investigations. We look at ethical frameworks that stress algorithmic fairness, data minimization, and human oversight to make sure they follow international standards like GDPR, ISO/IEC 27037, and AI ethics guidelines. This chapter envisions a comprehensive model of machine learning-enabled sustainable forensics by integrating green computing principles with ethical AI governance, fostering a balanced ecosystem that guarantees efficiency, transparency, and environmental stewardship in the age of intelligent investigations.