Summary: 1. Introduction. – 2. Methodology. – 3. Bias in the Judiciary: Human, Algorithmic, and Institutional Dimensions. – 3.1. A Human Bias. – 3.2. Judicial Bias: Sources and Safeguards. – 3.3. Automation Bias. – 4. AI and LLMs in courts. – 4.1. Deployment Models and Practices in the Judiciary. – 4.2. Problem Cases: Bias, Opacity, and Due-Process Risks. – 5. Bias Controls in Judicial AI: Hard- and Soft-Law Approaches. – 6. Conclusions.
Background: Courts are increasingly experimenting with large language models (LLMs) for tasks such as legal retrieval, drafting support, anonymisation, and triage. Yet the promise of efficiency collides with a structural problem: bias. Human adjudication already reflects cognitive and institutional biases; LLMs trained on past judgments and legal text inherit and sometimes amplify those biases. This article asks a focused question: If AI belongs in courts at all, what is the safe, lawful, and useful lane—especially with respect to bias? The inquiry is situated within fair-trial guarantees and emerging regulatory expectations.
Methods: A staged analysis grounded in legal obligations and informed by relevant technical characteristics is employed. First, sources of human and judicial bias are mapped, along with points at which LLMs introduce or magnify bias. Second, hard- and soft-law guardrails relevant to bias control in the justice sector are synthesised. Third, two instructive case studies—COMPAS/Loomis (U.S.) and Ewert v. Canada—are examined to demonstrate how group-level disparities and model opacity can generate due-process risks and to identify remedies transferable to LLM-assisted workflows. Finally, an operational blueprint is derived and applied to identify low-risk, high-yield assistive uses for Ukraine.
Results and conclusions: The analysis shows that fully impartial AI outputs are not attainable in adjudication; bias is ineliminable but can be bounded. For Ukraine, the rational path is to invest first in data curation, secure infrastructure, evaluation capacity, and procurement with audit rights, and to confine AI to retrieval, norm collation, drafting-hygiene checks, and “missed-norms” prompts. The contribution is a governance blueprint that ties specific LLM failure modes to enforceable legal duties and practical safeguards—offering courts a credible, bias-aware lane for AI that improves service while preserving rights.