Call for papers
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.
Reproducibility
Authors are strongly encouraged to publish their code, their data (including schemas) and their non-confidential prompting templates in open repositories alongside their submissions.
Studies using proprietary, closed-source LLMs, such as commercial APIs, should benchmark their performance against at least one open-weights, publicly available model of comparable scale under identical evaluation conditions. Programme committee members are instructed to take reproducibility into account in their reviews, alongside the guidelines above.
Review and anonymity
Reviewing is double-anonymised. Papers submitted for review must not include the names and affiliations of the authors and must not include an acknowledgements section. Any identifying text in the body of the paper — for example citing "our work" — should be removed or rephrased to be non-identifying; these aspects can be added at the camera-ready stage.
Prior to submission, authors must first register the paper on the conference support system to receive an ID number. The paper must then be revised so that the paper ID replaces the names and affiliations of the authors.
References should include the relevant published literature, including previous works by the authors, with a writing style that preserves anonymity. References to code and data intended for publication must also be phrased to maintain anonymity, for example by using anonymous GitHub services or OSF links.
Submitted papers may not be published as open access preprints before acceptance notifications have been sent.
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.
Publication and open access
ICAIL proceedings have traditionally been published by ACM in their conference proceedings series. As of 2026, all conference papers published by ACM are subject to mandatory open access requirements.
The open access fee is not included in the conference registration fee, and it is not sponsored by the IAAIL. Most universities, however, have agreements in place with ACM that waive it — see the ACM Open participants list. If none of a paper's authors is affiliated with an institution that has an ACM Open agreement, the authors are responsible for covering the fee. Temporary ACM subsidies or hardship waivers may apply depending on your region; if you think that is your case, check directly with ACM, for instance via the policy on open access APC waivers and discounts.
At least one author of an accepted paper must register for the conference for the paper to be presented and included in the proceedings. ICAIL 2027 is an in-presence event; remote participation is accepted only under exceptional circumstances, and at least one author of an accepted paper must present the work at the conference in person.
Accepted papers may also be considered for one of the three IAAIL awards, which are presented at the conference banquet.
Ethics and policies
Generative AI tools used to draft papers
For ICAIL 2027 we adhere to the principles and guidelines of the ACM Policy on Authorship, specifically its criteria for authorship regarding the use of generative AI technologies:
Generative AI tools and technologies, such as ChatGPT, may not be listed as authors of an ACM published Work. The use of generative AI tools and technologies to create content is permitted but must be fully disclosed in the Work. For example, the authors could include the following statement in the Acknowledgements section of the Work: ChatGPT was utilized to generate sections of this Work, including text, tables, graphs, code, data, citations, etc. If you are uncertain about the need to disclose the use of a particular tool, err on the side of caution, and include a disclosure in the acknowledgements section of the Work. (…) Basic word processing systems that recommend and insert replacement text, perform spelling or grammar checks and corrections, or systems that do language translations are to be considered exceptions to this disclosure requirement and are generally permitted and need not be disclosed in the Work. As the line between Generative AI tools and basic word processing systems like MS-Word or Grammarly becomes blurred, this Policy will be updated.
Authorship
All individuals who have made significant contributions to a paper — and only those — should be listed as authors in the submission system. Authors may not be added or removed after the submission deadline, so please also make sure that the order of authors is correct at submission time.
Disputes about authorship and misconduct should be negotiated and resolved by the parties to the dispute. IAAIL and ICAIL do not accept responsibility to adjudicate or resolve any such disputes.
Ethics and compliance
Research reported at ICAIL must adhere to high standards of academic integrity, honesty and trustworthiness, including the prevention of harm, data falsification and plagiarism, while respecting privacy, fairness and intellectual property. Submissions are subject to all applicable ACM publications policies and to the ACM Code of Ethics. Submitting a work to the conference implies acceptance of all the conditions set out in this call.
Paper assessment
Decisions on the acceptance or rejection of a paper fall under the exclusive responsibility of the Programme Chair, who has the final say. No disputes will be admitted.
Doctoral consortium
The Trevor Bench-Capon Doctoral Consortium aims to promote the exchange of ideas among PhD researchers in the area of artificial intelligence and law, and to give them an opportunity to interact with, and receive feedback from, leading scholars and experts in the field.
Since 2025 the doctoral consortium bears the name of Trevor Bench-Capon, one of the pillars of the AI and law community and one of the greatest supporters of the doctoral consortium initiative.
Details of the consortium's programme and timeline are published separately from this call, and will appear here. If you are a PhD student considering taking part and have a question in the meantime, write to contact@icail-vienna-2027.org.