Risks and Legal Regulation of Automated Sentencing Models in Environmental Justice

Abstract

The use of artificial intelligence in legal decision-making has created new possibilities for consistency, speed and structured reasoning, but it also raises serious concerns when algorithmic systems influence sentencing. This article examines the risks and legal regulation of automated sentencing models in the specific context of environmental justice. Environmental offences such as illegal pollution, hazardous waste disposal, deforestation, industrial contamination and wildlife crime often create cumulative ecological harm and unequal social burdens. These burdens frequently fall on communities that already face weak participation in environmental governance and limited access to remedies. The article adopts a doctrinal, comparative and normative legal research method. It analyses selected case law, international instruments, AI governance frameworks, environmental justice literature and data-protection standards. The article finds that automated sentencing models may reproduce biased enforcement data, obscure legal reasoning, encourage automation bias, weaken due process and fail to account for cumulative community harm. It argues that the problem is not simply whether AI is accurate, but whether its use is transparent, contestable, accountable and sensitive to environmental justice. The article concludes that automated sentencing systems should not replace judicial discretion. They may be used only as carefully controlled decision-support tools subject to mandatory algorithmic impact assessment, independent audit, explainability, meaningful human oversight, community participation and a right to challenge algorithmic outputs.