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Open research questions in Artificial Intelligence in Law

202 unresolved questions extracted from the limitations and future-work sections of 576 Artificial Intelligence in Law papers in our library. Each links back to the study that raised it.

What the literature leaves open

  • IMLJD provides the first structured, labelled, and reproducible computational dataset for Indian matrimonial litigation, built from public judicial archives. The dataset surfaces a 19.6-point quash success rate differential between SC and HC levels on a matched time period, a 15% settlement rate at the quash petition stage, and year-level trends in litigation volume. All data, code, and the knowledge graph are released openly. The pipeline is designed for extension to additional High Courts and future years. Researchers using this dataset should note that quash success rates do not indicate the veracity of underlying allegations. A quashed FIR reflects a procedural determination, not a factual finding. The metadata-derived indicators are descriptive starting points for hypothesis generation, not validated classifiers. Downstream applications that use this dataset to argue that matrimonial complaints are systematically false would misrepresent both the data and the legal process. Future work includes OCR-based extraction of full-text Supreme Court judgments, extension to additional High Courts, rhetorical-role labelling, and citation-outcome analysis. Full-text extraction would enable deeper analysis of judicial reasoning patterns, including procedural grounds for dismissal, maintenance rejection, and settlement-linked quashing. Integration with APIs such as Indian Kanoon or improved OCR pipelines may support richer precedent analysis and temporal litigation pathways.

    IMLJD: A Computational Dataset for Indian Matrimonial Litigation Analysis · 2026

  • Abstract Large Language Models (LLMs) are being integrated into professional domains, yet their limitations in such high-stakes fields as law remain poorly understood.

    Challenges for generative AI in legal reasoning · 2026 · DOI

  • Future research should focus on testing AI systems in diverse judicial contexts to address these issues.

    Improving the trial efficiency of criminal cases with the assistance of artificial intelligence · 2025 · DOI

  • Future research should explore underlying factors influencing these perceptions to inform policies that address racial disparities and enhance trust in AI-assisted legal decision-making.

    Public Perceptions of Judges’ Use of AI Tools in Courtroom Decision-Making: An Examination of Legitimacy, Fairness, Trust, and Procedural Justice · 2025 · DOI

  • Furthermore, the framework serves as a foundation for future research in the field of legal prompting with GLMs, and several avenues for future research are recommended in this paper.

    Standardized nomenclature for litigational legal prompting in generative language models · 2024 · DOI

  • The judicial system faces numerous difficulties, including logistical and systemic challenges. The use of AI in virtual court procedures can provide an alternative to human intervention.

    Using artificial intelligence technology in virtual court procedures · 2026 · DOI

  • The challenge of ensuring that AI systems can accurately identify parties to litigation and verify their identity while maintaining legal validity equivalent to human verification.

    Using artificial intelligence technology in virtual court procedures · 2026 · DOI

  • Future research should investigate the optimal injection ratio for different model capacities. Further studies should explore the application of the proposed framework to other judicial tasks.

    The trade-off between robustness and reliability in chinese legal large language models: an empirical study · 2026 · DOI

  • There is a gap in research on the dialectical relationship between robustness and reliability in the judicial context. Prior work has focused on task-specific applications, neglecting the trade-off between robustness and reliability.

    The trade-off between robustness and reliability in chinese legal large language models: an empirical study · 2026 · DOI

  • Build a small, targeted add-on training set (200–500 items) focused on provisos, negation, and cross-references from the Civil Code and past questions.

    Hybrid Legal Reasoning Approaches for COLIEE 2025 · 2026 · DOI

  • An overly large retrieval pool introduces substantial noise into the inference stage, overwhelming the LLM with irrelevant or weakly related paragraphs, while very small pools risk missing true positive evidence.

    Hybrid Legal Reasoning Approaches for COLIEE 2025 · 2026 · DOI

  • The regulation of AI in the legal profession is a complex issue. The current regulatory frameworks are insufficient to address AI-related issues. The need for a co-regulatory institutional framework to regulate AI responsibly.

    The Regulation of Artificial Intelligence in the Legal Profession · 2026 · DOI

  • The lack of a holistic solution to the problems presented by AI in the legal profession. The insufficiency of current regulatory frameworks to address AI-related issues.

    The Regulation of Artificial Intelligence in the Legal Profession · 2026 · DOI

  • The study identifies challenges in the use of AI in legal contexts, including the risk of algorithmic bias and the need for human oversight. It highlights the limitations of human decision-making, including susceptibility to systematic biases. The study also identifies challenges in the development of AI systems that can evaluate subjective experience and contextual nuances.

    Artificial Intelligence and Human Intelligence in Legal Systems · 2026 · DOI

  • Future research should focus on the development of AI systems that can mitigate the limitations of human decision-making. It should examine the implications of AI-enhanced decision-making for judicial procedures and the delivery of justice.

    Artificial Intelligence and Human Intelligence in Legal Systems · 2026 · DOI

  • The lack of transparency and accountability in pre-litigation analyses. The potential for biases in AI models used for pre-litigation analyses.

    An agentic AI marketplace for prelitigation analyses with ZKP-integrated ethical verifications · 2026 · DOI

  • The system architecture aligns proof generation with discrete audit events rather than per-query inference, but the paper does not empirically validate the latency and throughput characteristics of this batch-oriented approach when multiple litigants request simultaneous demographic threshold verifications in a high-volume prelitigation marketplace.

    An agentic AI marketplace for prelitigation analyses with ZKP-integrated ethical verifications · 2026 · DOI

  • Further exploration of hybrid approaches and their potential to improve performance, - Investigation of methods to address the hallucination issue in LLM-only solutions

    Legal citation prediction with LLMs: a comparative evaluation of instruction tuning, retrieval, and jurisdiction-specific pre-training on the AusLaw citation benchmark · 2026 · DOI

  • The lack of a comprehensive and jurisdiction-specific benchmark for legal citation prediction, - The need for more effective methods to address the challenges of factual accuracy and domain-specific reasoning

    Legal citation prediction with LLMs: a comparative evaluation of instruction tuning, retrieval, and jurisdiction-specific pre-training on the AusLaw citation benchmark · 2026 · DOI

  • The integration of serverless cloud architectures with GenAI legal NLP workloads remains largely absent, - The need for annotated legal corpora is a significant barrier

    Automated Legal Clause Extraction and Risk Scoring Using NLP and Generative AI · 2026 · DOI

  • The paper identifies the challenge of ensuring judicial impartiality when using AI. The paper highlights the challenge of avoiding mistakes in court decisions when using AI. The paper discusses the challenge of balancing the benefits of AI with the need to protect human rights.

    Court Proceedings and Artificial Intelligence - New Horizons · 2026 · DOI

  • The paper identifies the need for further analysis of AI applications in court proceedings. The paper highlights the gap in understanding the advantages and weaknesses of AI in court proceedings.

    Court Proceedings and Artificial Intelligence - New Horizons · 2026 · DOI

  • Traditional legal systems are often slow, expensive, and dependent on manual effort - The need for an automated solution that can simplify and accelerate legal processes

    Legal Ease AI · 2026 · DOI

  • The high level of domain specificity and structural complexity of legal documents. The need to leverage the strengths of traditional methods and pretrained language models to enhance global semantic representations.

    ALTER: a lightweight topic-aware representation legal case retrieval system · 2026 · DOI

  • Traditional information retrieval methods rely on sparse retrieval paradigms based on term frequency statistics. The proposed system, ALTER, addresses the gap by incorporating topic modeling and a Co-Attention layer to enhance global semantic representations.

    ALTER: a lightweight topic-aware representation legal case retrieval system · 2026 · DOI

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202 open questions have been extracted from the limitations and future-work passages of 576 Artificial Intelligence in Law papers in our library. Each one below links back to the study that raised it, so you can read the original claim in context.

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