Call for Papers: International Conference on Artificial Intelligence and Law (ICAIL), Papers are due by 28 January 2027
Since 1987, the International Conference on Artificial Intelligence and Law (ICAIL) has been the foremost global conference addressing research at the intersection of artificial intelligence and law. It is organised under the auspices of the International Association for Artificial Intelligence and Law (IAAIL). In 2025 the conference changed from a biennial to an annual schedule. The 22nd edition coincides with the 40th anniversary of the ICAIL series and takes place in Vienna, Austria.
We invite submissions of original research papers on artificial intelligence and law, covering foundations, concepts, methods, systems, tools and applications. Papers are due ; authors are notified on . Every deadline is listed under relevant dates.
Topics of interest
Topics of interest include, but are not limited to, the following four groups.
Foundations, knowledge representation and computational legal theory
- Logic and argumentation. Deontic and potestative logics, defeasible reasoning, argumentation frameworks, and models of normative conflict.
- Normative modelling. Formal or conceptual modelling of fundamental legal aspects, such as normative positions, causation, responsibility or legal qualification.
- Rule- and case-based reasoning. Formal and computational models of rule-based, case-based, evidential and value-based reasoning.
- Agent systems and norm emergence. Normative reasoning by autonomous agents, normative multi-agent systems, computational social science in law, and complex adaptive systems modelling of legal ecosystems.
- Neuro-symbolic integration. Approaches merging deep learning with symbolic legal knowledge representation and hybrid reasoning models, for example the autoformalisation of natural-language legal texts into computable logic or domain-specific languages.
- Ontologies and standards. Formal models of norms, legal ontologies, semantic web mark-up languages, open linked data, and legal data standards and interoperability schemas.
Legal data science, information retrieval and generative AI
- Legal NLP. Precedent-aware named entity recognition, semantic role labelling, multilingual legal corpora, parsing of legal text, and information extraction from text.
- Information retrieval, search and network analysis. Legal information retrieval, semantic search, recommender systems, and structural network analysis of legal systems — statutory citation graphs, court precedent network topology, and topological data analysis of legal corpora.
- Argument mining. Argument mining on unstructured legal texts and automated information extraction from legal databases.
- Predictive analytics and other empirical methods. Predictive legal analytics, multi-modal legal data processing, and empirical machine learning methods applied to statutory and case law.
- Generative AI, evaluation and verification. LLM applications tailored for complex legal reasoning and synthesis, accompanied by rigorous evaluation pipelines, legal benchmarking, hallucination management and verification methods.
Technical governance, legal risk analysis and normative alignment
- Regulatory compliance engineering. Formal and technical compliance-checking systems, logic-based verification, and runtime compliance for dynamic digital and AI regulatory environments, such as automated adherence to legal frameworks and end-to-end compliance architectures.
- Algorithmic fairness and bias. Bias assessment, fairness metrics, and non-discrimination design embedded in legal, judicial and administrative tools.
- Law-based AI safety, alignment and guardrails. Risk mitigation, model guardrails, norm-aware reinforcement learning, and law-following agent architectures designed to operate within legal constraints.
- Legal risk assessment and auditability. Automated legal risk assessment, exposure modelling, liability allocation, and explainable AI for judicial and administrative accountability and auditability.
- Accountability operationalisation. Operationalisation strategies for meaningful human control and other accountability frameworks, integrating normative systems, legal accountability and human oversight into high-stakes automated decisions.
Legal technologies
- Hybrid intelligence workflows. Human-in-the-loop, human-on-the-loop and other legal workflow systems for judges, attorneys, legal practitioners and other relevant stakeholders.
- Access to justice and public interest technology. AI-driven access to justice tools, public interest legal technologies, and participatory data infrastructures.
- Rules as Code and e-government. Automation of the state, computational governance (“Rules as Code”), and e-justice and e-democracy platforms.
- Dispute resolution and negotiation. Computer-assisted and online dispute resolution, computational negotiation methods, and automated contract formation.
- Smart contracts and distributed ledgers. Formal, computational and jurisprudential challenges of smart contracts, DAOs and decentralised dispute resolution.
- Legal process, forensics and education. Legal process mining, digital forensics, evidence evaluation technology, and intelligent legal tutoring systems.
Paper submission
Paper length
- Long papers — up to 10 pages, including references.
- Short papers — up to 5 pages, including references.
- Demonstrations (extended abstracts) — up to 2 pages, including references.
Scope
Submissions must present contributions on topics relevant to AI and law, such as those listed above. To maintain ICAIL’s characterisation within the broader, rapidly moving field at the intersection of law and artificial intelligence, we will not accept submissions focused exclusively on the regulation of technology, on policy, or on legal doctrine. We do welcome papers engaging in scholarly elaborations on legal concepts and processes when those are framed in a computational or technical context.
We will also not accept papers that merely apply off-the-shelf LLMs to hand-picked simplistic cases, lacking systematic and rigorous methodology, scalable benchmarking and legal-domain analytical depth.
Guidelines
Authors must include a clear statement detailing the novel scientific contribution of the work. The relationship to prior work — including work at AI and law venues such as ICAIL, JURIX and the Artificial Intelligence and Law journal — must be thoroughly developed, and papers should feature an adequate discussion comparing their findings to it.
Depending on the type of paper, additional guidelines apply:
- Formal or computational models. Papers should include concrete examples, such as a sound use case, a realistic legal conflict or a genuine statutory nuance, or reproducible simulations. Authors should provide clear syntax and semantics and, where applicable, proof sketches or theoretical evaluations of the relevant properties.
- Data mining, machine learning and generative AI. Papers must go beyond merely reporting metrics: they should discuss the relevant legal background, the data, the methodology, the results and the analysis. Evaluations should use authentic, uncurated or multi-jurisdictional legal data — actual case law, messy contracts, administrative filings — rather than purely synthetic or overly sanitised benchmark subsets.
- Applications. Papers must clearly describe the motivations, techniques, implementation and evaluation, whether that evaluation is user-centred, technical or otherwise.
- Human evaluations. Where human evaluation is involved, annotations must be performed or validated by qualified legal professionals, and inter-annotator agreement — Cohen’s or Fleiss’ kappa, for instance — must be reported.
Format and submission
Papers must be formatted using the ACM sigconf template (for LaTeX) or the interim template layout.docx (for Word), both at acm.org/publications/proceedings-template. All papers should be converted to PDF prior to electronic submission. Note that some platforms provide templates that are not fully compliant with the official ACM template.
Papers that do not adhere to these conditions, including the page limitations and the anonymity requirements, may be rejected without review.
Submissions should be uploaded to the conference support system by the submission deadline. The submission platform will be announced here in good time before the deadline. If you have any questions about the submission process, please write to contact@icail-vienna-2027.org.
