About
Adam is an Associate Professor in Law and Computer Science, and the programme lead on the LLM in LegalTech.
Related pages
- Hillary Rodham Clinton School of Law Staff
- Amaxilati, Zoumpoulia
- Aston, Val
- Atkins, Ruth
- Barazza, Stefano
- Baughen, Simon
- Beale, Andrew
- Bishop, Patrick
- Borland, Emma
- Brown, Lloyd
- Bussicott, Hannah
- Carlile, Lord
- Coleman, Michelle
- Conway, Maura
- Correia, Sara
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- Macdonald, Stuart
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- Quane, Helen
- Rees, Amanda
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- Rees, Trish
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- Seagrim, Billy
- Soyer, Baris
- Staniforth, Andrew
- Tettenborn, Andrew
- van Logchem, Youri
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- Wyner, Adam
- Yilmaz, Kamil
- School of Management Staff
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Dr Adam Wyner
Associate Professor,
Computer Science
Telephone number
+44 (0) 1792 295308
Research Links
Areas Of Expertise
- Artificial Intelligence and Law
- Natural Language Processing
- Argumentation
- Knowledge representation
- Knowledge acquisition
- Reasoning
- Semantic Web
- Knowledge graph
Publications
Research Highlights
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Strass, H., Wyner, A., & Diller, M. (2019). EMIL: Extracting Meaning from Inconsistent Language. International Journal of Approximate Reasoning, 112, 55-84.
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Liepiņa, R., Sartor, G., & Wyner, A. (2020). Arguing about causes in law: a semi-formal framework for causal arguments. Artificial Intelligence and Law, 28(1), 69-89.
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Shulayeva, O., Siddharthan, A., & Wyner, A. (2017). Recognizing cited facts and principles in legal judgements. Artificial Intelligence and Law, 25(1), 107-126.
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Fawei, B., Pan, J., Kollingbaum, M., & Wyner, A. (2018). A Methodology for a Criminal Law and Procedure Ontology for Legal Question Answering. In Semantic Technology (pp. 198-214).
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Soares, R., Edelstein, E., Pan, J., & Wyner, A. (2018). Knowledge Driven Intelligent Survey Systems for Linguists. In Semantic Technology-18). Semantic Technology: 8th Joint International Conference, JIST 2018..
All Research
Journal Articles
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Pan, J., Edelstein, E., Bansky, P., & Wyner, A. (2021). A Knowledge Graph Based Approach to Social Science Surveys. Data Intelligence, 3(4), 477-506.
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Liepiņa, R., Sartor, G., & Wyner, A. (2020). Arguing about causes in law: a semi-formal framework for causal arguments. Artificial Intelligence and Law, 28(1), 69-89.
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Fawei, B., Pan, J., Kollingbaum, M., & Wyner, A. (2019). A Semi-automated Ontology Construction for Legal Question Answering. New Generation Computing, 37(4), 453-478.
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Strass, H., Wyner, A., & Diller, M. (2019). EMIL: Extracting Meaning from Inconsistent Language. International Journal of Approximate Reasoning, 112, 55-84.
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Shulayeva, O., Siddharthan, A., & Wyner, A. (2017). Recognizing cited facts and principles in legal judgements. Artificial Intelligence and Law, 25(1), 107-126.
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Wyner, A. (2016). Working on the argument pipeline: Through flow issues between natural language argument, instantiated arguments, and argumentation frameworks. Argument & Computation, 7(1), 69-89.
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Wyner, A. (2016). A functional perspective on argumentation schemes. Argument & Computation, 7(2-3), 113-133.
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Prakken, H., Wyner, A., Bench-Capon, T., & Atkinson, K. (2015). A formalization of argumentation schemes for legal case-based reasoning in ASPIC+. Journal of Logic and Computation, 25(5), 1141-1166.
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Lin, C., Ibeke, E., Wyner, A., & Guerin, F. (2015). Sentiment-topic modeling in text mining. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 5(5), 246-254.
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Wyner, A., Bench-Capon, T., Dunne, P., & Cerutti, F. (2015). Senses of ‘argument’ in instantiated argumentation frameworks. Argument & Computation, 6(1), 50-72.
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Wyner, A. & Hoekstra, R. (2012). A legal case OWL ontology with an instantiation of Popov v. Hayashi. Artificial Intelligence and Law, 20(1), 83-107.
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Wyner, A., Bench-Capon, T., & Wyner, A. (2009). Modelling Judicial Context in Argumentation Frameworks. Journal of Logic and Computation, 19(6), 941-968.
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Wyner, A. (2008). An ontology in OWL for legal case-based reasoning. Artificial Intelligence and Law, 16(4), 361-387.
Authored Books
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Wyner, A. & Strass, H. (2017). Advances in Artificial Intelligence: From Theory to Practice Springer Verlag.
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Wyner, A. (2017). Book chapter: On annotation of the textual contents of Scottish legal instruments IOS Press.
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Wyner, A., Nazarenko, A., & Lévy, F. (2016). Controlled Natural Language Springer.
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Wyner, A. (2016). Towards a methodology for formalizing legal texts in LegalRuleML IOS Press.
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Bench-Capon, T., Atkinson, K., & Wyner, A. (2015). Using Argumentation to Structure E-Participation in Policy Making. Springer.
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Wyner, A. (2015). Passing a USA national bar exam - A first experiment IOS Press.
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Wyner, A., Bench-Capon, T., & Dunne, P. (2013). Computational Logic in Multi-Agent Systems
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Athan, T., Boley, H., Governatori, G., Palmirani, M., Paschke, A., & Wyner, A. (2013). Theory, Practice, and Applications of Rules on the Web
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Wyner, A. (2013). Argumentation schemes for reasoning about factors with dimensions
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Wyner, A. (2013). A case study on legal case annotation
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Wyner, A. (2012). Semi-automated argumentative analysis of online product reviews
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Wyner, A., Atkinson, K., & Bench-Capon, T. (2012). Electronic Participation
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Wyner, A. (2012). Critiquing justifications for action using a semantic model: Demonstration
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Schneider, J., Davis, B., & Wyner, A. (2012). Knowledge Engineering and Knowledge Management
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Wyner, A. (2012). A model-based critique tool for policy deliberation
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Wyner, A., Riley, L., Hoehndorf, R., & Croset, S. (2012). Information Technology in Bio- and Medical Informatics
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Lloyd-Kelly, M. & Wyner, A. (2012). Advances in User Modeling
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Wyner, A. (2012). An empirical approach to the semantic representation of laws
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Wyner, A. (2011). On rule extraction from regulations
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Wyner, A., Atkinson, K., & Bench-Capon, T. (2011). Electronic Participation
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Wyner, A. (2011). Populating an online consultation tool
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Wyner, A., Angelov, K., Barzdins, G., Damljanovic, D., Davis, B., Fuchs, N., Hoefler, S., Jones, K., Kaljurand, K., Kuhn, T., Luts, M., Pool, J., Rosner, M., Schwitter, R., & Sowa, J. (2010). Controlled Natural Language
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Wyner, A. (2010). Lexical semantics and expert legal knowledge towards the identification of legal case factors
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Wyner, A., Mochales-Palau, R., Moens, M., & Milward, D. (2010). Semantic Processing of Legal Texts
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Wyner, A. (2010). Arguing Global Governance
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Wyner, A., van Engers, T., & Bahreini, K. (2010). Electronic Government and the Information Systems Perspective
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Wyner, A. (2008). Modelling judicial context in argumentation frameworksSU Repository: https://cronfa.swan.ac.uk/Record/cronfa50735
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Wyner, A. (2007). Towards an extensible argumentation systemSU Repository: https://cronfa.swan.ac.uk/Record/cronfa50737
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Wyner, A. (2007). Three senses of "argument"
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Wyner, A. (2006). A functional program for agents, actions, and deontic specificationsSU Repository: https://cronfa.swan.ac.uk/Record/cronfa50738
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Wyner, A. (2006). Deontic Logic and Artificial Normative Systems
Book Chapters
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Peters, W. & Wyner, A. (2015). Extracting Hohfeldian relations from text. In Antonino Rotolo (Ed.), Legal Knowledge and Information Systems (pp. 189-190). IOS Press
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Athan, T., Governatori, G., Palmirani, M., Paschke, A., & Wyner, A. (2015). LegalRuleML: Design Principles and Foundations. In Wolfgang Faber, Adrian Paschke. (Ed.), Reasoning Web. Web Logic Rules (pp. 151-188). Springer International Publishing
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Wyner, A., Bench-Capon, T., & Dunne, P. (2009). On the Instantiation of Knowledge Bases in Abstract Argumentation Frameworks. Computational Logic in Multi-Agent Systems-50).
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Wyner, A. (2004). Maintaining Obligations on Stative Expressions in a Deontic Action Logic. Deontic Logic in Computer Science-274).
Conference Contributions
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Edwards, J., Wintersberger, P., Clark, L., Rough, D., Doyle, P., Banks, V., Wyner, A., Janssen, C., & Cowan, B. (2021). CUI @ Auto-UI: Exploring the Fortunate and Unfortunate Futures of Conversational Automotive User Interfaces. In 13th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (pp. 186-189). ACM.
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Bansky, P., Edelstein, E., Pan, J., & Wyner, A. (2020). A Dynamic and Informative Intelligent Survey System Based on Knowledge Graph. In Semantic Technology (pp. 226-241). Springer International Publishing.
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Wyner, A. (2018). A Chatbot Framework for the Children's Legal Centre. In Legal Knowledge and Information Systems (pp. 205-209). IOS Press.SU Repository: https://cronfa.swan.ac.uk/Record/cronfa48661
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Fawei, B., Pan, J., Kollingbaum, M., & Wyner, A. (2018). A Methodology for a Criminal Law and Procedure Ontology for Legal Question Answering. In Semantic Technology (pp. 198-214).
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Soares, R., Edelstein, E., Pan, J., & Wyner, A. (2018). Knowledge Driven Intelligent Survey Systems for Linguists. In Semantic Technology-18). Semantic Technology: 8th Joint International Conference, JIST 2018..
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Wyner, A. (2017). On automated defeasible reasoning with controlled natural language and argumentation. In AAAI Publications, Workshops at the Thirty-First AAAI Conference on Artificial IntelligenceSU Repository: https://cronfa.swan.ac.uk/Record/cronfa50682
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Wyner, A. & Peters, W. (2016). Legal text interpretation: Identifying Hohfeldian relations from text. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016) (pp. 379-384).SU Repository: https://cronfa.swan.ac.uk/Record/cronfa50684
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Wyner, A. (2016). Passing a USA national bar exam: A first corpus for experimentation. LREC 2016, Tenth International Conference on Language Resources and Evaluation.SU Repository: https://cronfa.swan.ac.uk/Record/cronfa50685
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Wyner, A. (2014). Legal interpretations in LegalRuleML.SU Repository: https://cronfa.swan.ac.uk/Record/cronfa50694
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Wyner, A. (2014). Mining fine-grained argument elements.SU Repository: https://cronfa.swan.ac.uk/Record/cronfa50695
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Athan, T., Boley, H., Governatori, G., Palmirani, M., Paschke, A., & Wyner, A. (2013). OASIS LegalRuleML. In ICAIL '13 Proceedings of the Fourteenth International Conference on Artificial Intelligence and Law (pp. 3-12). International Conference on Artificial Intelligence and Law.
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Wardeh, M., Wyner, A., Atkinson, K., & Bench-Capon, T. (2013). Argumentation based tools for policy-making. In ICAIL '13 Proceedings of the Fourteenth International Conference on Artificial Intelligence and Law (pp. 249-250). International Conference on Artificial Intelligence and Law.
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Bench-Capon, T., Prakken, H., Wyner, A., & Atkinson, K. (2013). Argument schemes for reasoning with legal cases using values. In ICAIL '13 Proceedings of the Fourteenth International Conference on Artificial Intelligence and Law (pp. 13-22). International Conference on Artificial Intelligence and Law.
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Wyner, A. (2013). A study on translating regulatory rules from natural language to defeasible logic.SU Repository: https://cronfa.swan.ac.uk/Record/cronfa50697
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Wyner, A. (2012). Identifying consumers' arguments in text.SU Repository: https://cronfa.swan.ac.uk/Record/cronfa50712
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Wyner, A. (2012). Arguing from a point of view.SU Repository: https://cronfa.swan.ac.uk/Record/cronfa50708
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Atkinson, K., Bench-Capon, T., Cartwright, D., & Wyner, A. (2011). Semantic models for policy deliberation. In ICAIL '11 Proceedings of the 13th International Conference on Artificial Intelligence and Law (pp. 81-90). International Conference on Artificial Intelligence and Law.
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Wyner, A., Bench-Capon, T., & Atkinson, K. (2011). Towards formalising argumentation about legal cases. In ICAIL '11 Proceedings of the 13th International Conference on Artificial Intelligence and Law (pp. 1-10). International Conference on Artificial Intelligence and Law.
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Wyner, A. (2010). Web-based mass argumentation in natural language.SU Repository: https://cronfa.swan.ac.uk/Record/cronfa50730
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Wyner, A. (2010). Towards annotating and extracting textual legal case elements.SU Repository: https://cronfa.swan.ac.uk/Record/cronfa50729
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Wyner, A. (2010). What do you mean? Arguing for meaning.SU Repository: https://cronfa.swan.ac.uk/Record/cronfa50731
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Wyner, A. (2010). Representing deontic concepts for CNLs.SU Repository: https://cronfa.swan.ac.uk/Record/cronfa50728
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Wyner, A., Bench-Capon, T., & Dunne, P. (2009). On the Instantiation of Knowledge Bases in Abstract Argumentation Frameworks. In Computational Logic in Multi-Agent Systems (pp. 34-50).
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Liepina, R., Sartor, G., & Wyner, A. (n.d.) Causal Models of Legal Cases. In AI Approaches to the Complexity of Legal Systems-186).
Taught Modules
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CS-261 Coding for Lawyers
This module provides an introduction to Computer programming and coding principles, tailored to students from Law. Students will be able to apply those principles in practice to program development in Python and gain further insight in the typical design, structure and application of technical solutions. The module enables students majoring in Law to reach a level of skill in programming such that they will be able to apply their computing knowledge to their own subject.
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CSC325 Artificial Intelligence
CSC325 is an introduction to Artificial Intelligence, focusing primarily on reasoning and problem solving as a search for a solution rather than on statistical techniques for classification. The course may cover topics from amongst: search techniques; knowledge representation and expert systems; planning; scheduling; qualitative reasoning; language processing with grammar rules; and meta-programming, as well as agents, multi-agent systems, and agent collaboration.
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CSCM23 Designing-in Trust, Understanding and Negotiation
This module explores state-of-the-art methods and concepts to assist responsible design and development of technology with the aim of creating reliable and trusted systems. The content of this module will be delivered by expert lecturers and practitioners in the areas of trusted computation, bias and explainability in automated decision making and decision support, ethical considerations for AI, argumentation and negotiation, as well as formal methods, such as verification of critical systems.
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CSLM81 Computational Thinking
Computational Thinking is a problem solving paradigm aimed at constructing and evaluating solutions to problems which a computer - be it human or mechanical - can carry out effectively. The module provides an introduction to Computational Thinking and Computer Programming, tailored to students from Law. Students will be able to apply problem solving principles to program development in Python. They will gain insight into the typical design, structure and application of technical solutions within a legal context.
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LAAD01 LegalTech Project
Students will seek to develop a technological solution to a legal services challenge in commercial law or access to justice settings. Students will be able to use any software application or program language. Supervision will be provided in accordance with the procedures set out in the LLM LegalTech Handbook. Students should refer to the Handbook for information on supervision.
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LAAM19 Artificial Intelligence and Law - Principles
The module is an introduction to some of the principles and techniques of Artificial Intelligence as applied to legal information such as legislation, case law, and contracts. Students will learn some of the key elements of AI including logic, knowledge representation, natural language processing, and machine learning. Students will gain theoretical knowledge about AI systems to understand how they are used to analyse, represent, and process legal information.
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LAAM35 Artificial Intelligence and Law - Applications
The module introduces the students to IT technologies that may be concretely employed in the current LegalTech market. Special attention will be devoted to Semantic Web technologies for the legal domain, such as LegalDocML and RDF/OWL. These technologies have recently gained attention both in academia, industry, and standardization bodies. IT technologies based on semantics represent the path to explainability, which has been deemed by many as a desirable feature of next-generation AI technologies, in particular for LegalTech applications. The module also aims to encourage students to identify new use cases, which could be interesting for the current LegalTech industry and which could even set the ground for a future start-up project plan.
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LAAM37 Contemporary Issues in LegalTech
LegalTech is revolutionising the way in which legal services are designed and delivered all around the world. New technologies are introduced at a very fast pace, with existing ones evolving rapidly in response to increased market demand. Regulators and policy-makers constantly introduce new and amended regulations to accompany the technological evolution, while courts deal with complex questions of interpretation and application of existing and new frameworks of law to legal technologies. Law firms and start-ups introduce new legal services based on LegalTech innovation, driving a transformation of the legal sector that has a profound impact on our society. This module seeks to explore the most cutting-edge issues that accompany such transformation, analysing current developments, related to the introduction of new technologies, amended or new regulations and relevant case law. It aims to contextualise such changes, providing a basis for future research, and to encourage a critical discussion on their significance and impact, predicting future trends and further developments. The module also seeks to help students develop skills that are becoming increasingly important for LegalTech professionals, for example in relation to entrepreneurship, ethics and interdisciplinarity. The module will offer a mix of different learning opportunities, with research-based lectures, practical workshops, seminars, guest lectures and talks with LegalTech professionals. This variety reflects the nature of the LegalTech field, where academic research sits alongside practical implementation, with innovation taking place in the diverse environments of law firms, start-ups, universities, legal innovation laboratories and more. The assessment offers students a chance to reflect critically on the knowledge and skills acquired throughout the year, to carry out research on a LegalTech development of interest and to communicate such research in a presentation and blog post. This will allow students to develop important professional skills, including in research and communication, helping them become well-informed, engaged and responsible LegalTech professionals.