AI in the courtroom is coming, but judges and lawyers aren’t ready

A new legal framework under the European Union’s Artificial Intelligence Act is about to shake up how courts and legal professionals deploy artificial intelligence. A study titled “AI Literacy for Legal AI Systems: A Practical Approach”, forthcoming in Iustum Aequum Salutare (2025), warns that legal AI systems are entering judicial institutions faster than stakeholders are prepared to manage their ethical and operational risks. At the center of this preparedness is a mandatory but poorly defined requirement: AI literacy.

The research establishes a practical roadmap for courts, law firms, and other legal service providers to evaluate AI tools using structured literacy frameworks. These systems, ranging from outcome prediction engines to smart legal databases, are increasingly relied upon for case analysis, legal research, and document automation. But their unregulated use could undermine judicial impartiality, reinforce existing biases, and trigger widespread automation errors.

What Are Legal AI Systems and Why Are They High-Risk?

Legal AI systems refer to any artificial intelligence tool used in legal or judicial contexts. These include rule-based automation, machine learning systems, and increasingly, large language models (LLMs) used for predictive outcomes, legal argument generation, or courtroom simulations. According to the study, these systems, when used for case assessment, sentencing recommendations, or legal research, can influence fundamental rights and are therefore categorized as high-risk under the EU AI Act.

Annex III of the AI Act classifies AI systems in legal administration as inherently high-risk, owing to their impact on liberty, due process, and non-discrimination. Whether used by judges, clerks, or private law firms, their deployment necessitates human oversight, bias audits, and explainability features. However, the report reveals that current implementations often lack consistent oversight mechanisms and remain technically opaque, especially where LLMs are involved.

The legal AI landscape remains fragmented, with different European countries piloting tools like contract management AI in France, decision-prediction platforms in Germany, and voice-to-text analytics across digital courts. The diversity in applications complicates regulatory enforcement and heightens the urgency for domain-specific AI literacy - an awareness of the benefits, risks, and operational logic of such tools.

Why is AI literacy crucial for legal professionals?

The AI Act’s Article 4 mandates AI literacy (AI-L) for all providers, deployers, and users of AI systems, regardless of risk classification. But the study finds this requirement is poorly defined and inconsistently implemented. AI literacy, in this legal context, includes knowledge of ethical constraints, system limitations, legal obligations, and practical comprehension of AI-generated outputs.

Gültekin-Várkonyi frames AI-L as a three-tiered concept encompassing skills, knowledge, and understanding. These competencies must enable professionals to interpret AI decisions, foresee risks such as bias and hallucination, and respond appropriately. The goal is not just regulatory compliance but empowering legal actors to engage meaningfully with AI -a necessity in systems with potential to influence judicial decisions.

To support operationalization, the study introduces a roadmap questionnaire for organizations, asking targeted questions like: What human rights implications does the system entail? How are AI-generated decisions audited for fairness? What are the safeguards against overreliance or data hallucination?

What are the benefits and risks of legal AI deployment?

The paper outlines three main benefits of legal AI systems, which AI-L must help unlock:

1. Reduction of Human Bias: Algorithms, when well-designed and trained on diverse data, can eliminate emotional, cognitive, and cultural biases prevalent in human decision-making. This could enhance consistency and fairness in outcomes, particularly in cases where human judges are subject to stress or implicit bias.

2. Time and Cost Efficiency: AI systems can automate time-intensive legal processes such as document drafting, precedent analysis, and case management. This promises faster access to justice for individuals and reduced caseload burdens for judicial institutions.

3. Explainability: Properly designed AI systems can provide rationales for their outputs, improving legal transparency and enabling citizens to challenge decisions. Techniques such as explainable machine learning and natural language generation are seen as tools to align AI decisions with the rule of law.

However, these gains are shadowed by critical risks, which are often underestimated:

• Bias and Overreliance: Tools like the COMPAS recidivism algorithm have faced legal challenges due to embedded gender or racial biases. The anthropomorphic design of some systems can falsely reassure users of their objectivity, leading to “automation bias” or blind reliance on flawed outputs.

• Technical Limitations: Legal reasoning, particularly in civil law traditions, cannot be reduced to textual analysis alone. Current AI models struggle with interpreting legislative intent, handling unstructured data, and operating within jurisdiction-specific frameworks. The opacity of training data and internal logic further undermines explainability.

• Environmental and Economic Costs: Training large legal AI models consumes significant energy and computing resources. Moreover, their development remains costly, particularly for small firms and jurisdictions with limited access to structured legal data.

The study emphasizes that despite the AI Act’s legal requirements, technical solutions alone cannot resolve these concerns. Without structured AI-L programs, organizations risk compliance failures, erosion of public trust, and unintended human rights violations.

Toward a literacy-driven legal AI culture

The paper concludes with a pragmatic solution: integrating AI literacy at every stage of the AI system lifecycle. From procurement and design to deployment and post-decision review, organizations must embed AI-L into workflows and staff development. Inspired by models like GDPR’s data protection impact assessments, the author proposes AI-L programs based on questionnaire-driven evaluation, stakeholder engagement, and interdisciplinary training.

Furthermore, the author urges regulators and institutions to treat AI-L not merely as an educational goal but as an ethical infrastructure - a precondition for meaningful oversight, accountability, and human-centered AI in legal systems. Absent this shift, the benefits of AI in law could be eclipsed by a loss of legitimacy, widening inequalities, and systemic opacity.