This paper develops a normative and enforceable framework for governing predictive analytics in judicial decision-making. It argues that existing ethical guidelines—while valuable—remain insufficient without legal translation into binding standards capable of ensuring accountability, fairness, and procedural integrity. The study critically examines the use of predictive models in judicial contexts, highlighting key risks such as algorithmic bias, opacity, and the potential erosion of judicial discretion. It distinguishes between ethical principles and legal obligations, emphasizing the need to transform soft-law guidance into operational legal standards. Adopting a doctrinal and analytical approach, the research proposes a structured framework that integrates core principles—transparency, explainability, accountability, non-discrimination, and human oversight—into enforceable legal criteria. It further explores how these standards can be applied within judicial systems through audit mechanisms, risk classification, and procedural safeguards. The paper contributes to Legal Tech scholarship by bridging the gap between AI ethics and legal regulation, offering a model that aligns predictive technologies with the fundamental requirements of justice and the rule of law.
Amal Fawzy Ahmed Awad (Sat,) studied this question.