The escalating integration of algorithmic systems into criminal justice raises profound ethical questions that demand careful consideration. While proponents emphasize the potential for these systems to reduce human bias and improve efficiency, critics caution against the inherent risks of perpetuating and amplifying existing societal inequalities. These risks stem from the fact that algorithms are trained on historical data, which often reflects discriminatory practices prevalent in policing, prosecution, and sentencing. Consequently, if left unchecked, these systems could lead to biased outcomes, disproportionately affecting marginalized communities. It would have been preferable had these datasets been cleaned of bias before implementation. The challenge lies in ensuring transparency and accountability in algorithmic decision-making. Because algorithms are often complex and opaque, it can be difficult to identify and mitigate sources of bias. Moreover, the reliance on automated systems may erode due process protections and undermine public trust in the justice system. Some argue that algorithmic tools should be subject to rigorous testing and auditing to ensure fairness and accuracy. Furthermore, mechanisms should be in place to allow individuals to challenge algorithmic decisions and seek redress for any harm caused. Ignoring such precautions could have detrimental effects. Ultimately, the ethical use of algorithms in criminal justice requires a commitment to equity, fairness, and transparency, as well as ongoing critical evaluation of their impact on society.