The integration of Artificial Intelligence (AI) in judicial decision-making processes has introduced both opportunities and significant concerns, particularly regarding fairness and transparency. This paper critically examines the phenomenon of algorithmic inequity within legal systems, focusing on how biased data, opaque algorithms, and lack of accountability can perpetuate or even amplify existing social injustices. Through interdisciplinary analysis, the study explores the structural factors contributing to algorithmic bias, its implications for marginalized communities, and the ethical dilemmas facing policymakers and technologists. Case studies of real-world AI applications in sentencing, parole, and risk assessment highlight the societal consequences of uncritical AI adoption in the justice system. The paper concludes with recommendations for fostering algorithmic accountability, inclusive data governance, and human oversight to ensure equitable and trustworthy judicial outcomes.