Ethical Frameworks for Algorithmic Decision-Making in Criminal Justice Systems

Abstract

The adoption of Algorithmic Decision-Making (ADM) in criminal justice systems has raised significant ethical questions, particularly regarding fairness, transparency, and accountability. This paper discusses different ethical theories of assessing and reducing the risks of using algorithms in criminal justice. To determine the major ethical principles, including justice, non-discrimination, and the right to explanation, necessary to regulate the creation and implementation of algorithms in the most sensitive fields such as sentencing, parole, and risk assessment, a systematic review of the available literature was conducted. The paper uses the comparative study of four major ethical theories, including deontological ethics, utilitarianism, virtue ethics, and care ethics, determining their applicability and constraints in algorithmic justice. Based on statistical findings of existing case studies, the differences in the performance of the algorithms can be noted, such as racial biases and the absence of transparency in the course of decision-making. These findings indicate that the Ethical ADM System performed better than the standard ADM system, with an accuracy of 92.1% as opposed to 85.3% for the standard system. The results indicate that a balanced approach to ethics would provide the most appropriate way of reducing risks and increasing justice. The paper concludes with a set of recommendations to policy-makers and stakeholders on how to create a strong ethical framework for the responsible application of ADM in criminal justice, the importance of constant supervision, and the need to focus on human rights protection.