AI in the Courtrooms: Global Expert Systems, Ethical Challenges, and Policy Implications for Regulating the Use of AI
Bibliographic record
Abstract
Purpose This article examines the growing integration of artificial intelligence (AI)–based expert systems in judicial and legal processes across major jurisdictions and critically evaluates their advantages and ethical challenges. It aims to analyse how courts and legal professionals worldwide are deploying AI to enhance efficiency, access to justice, and case management, while assessing the risks such technologies pose to transparency, accountability, fairness, and human discretion in adjudication. Design/methodology The study adopts a qualitative doctrinal and comparative research methodology. It analyses legal frameworks, judicial guidelines, policy documents, and reported case law from multiple jurisdictions, including the United States, Canada, Australia, the European Union, and India. The paper also reviews existing AI expert systems used by courts and legal practitioners, such as predictive analytics tools, legal research platforms, automated filing systems, and generative AI applications, to evaluate their functional scope and regulatory treatment. Findings The study finds that AI expert systems have significantly improved judicial efficiency by automating clerical tasks, streamlining legal research, and supporting case management. However, their use raises serious concerns about algorithmic bias, opaque decision-making, privacy violations, accountability gaps, and the risk of hallucinated or fabricated legal authorities. Jurisdictions that have adopted structured regulatory approaches— particularly the European Union and Canada—demonstrate stronger safeguards through transparency obligations and the “human-in-the-loop” principle. Conversely, inconsistent or fragmented regulation increases the risk of misuse and over-reliance on AI in judicial contexts. Practical implications The paper highlights the need for robust ethical guidelines, judicial training, and regulatory frameworks to ensure responsible use of AI in legal systems. AI should function strictly as an assistive tool, with final decision-making authority remaining with human judges and lawyers. The study also underscores the urgent need for coherent policy frameworks governing the use of artificial intelligence in judicial systems. It highlights the importance of adopting structured regulatory models that incorporate principles such as transparency, accountability, explainability, data protection, and the “human-in-the-loop” approach. Originality/value This article offers a comprehensive comparative analysis of global AI deployment in courtrooms. It contributes to the evolving discourse on balancing technological innovation with judicial ethics, constitutional values, and procedural fairness.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".