The MLLD workshop aims to bring together researchers and practitioners to share the latest advances in applying data mining, machine learning, information retrieval, and knowledge management techniques to the legal sector. Compared with other application domains, the legal domain is characterized by the huge scale of natural language text data, the high complexity of specialist knowledge, and the critical importance of ethical considerations. This creates unique opportunities to develop data-driven techniques and advanced tools for a variety of tasks in the legal domain, such as legal search and research, legal document review and summary, legal contract drafting, and legal outcome prediction. Building upon the previous successes, the third edition of the MLLD workshop will emphasize the exploration of new research opportunities brought about by Large Language Models and Generative AI. We encourage submissions that intersect computer science and law, from both academia and industry, embodying the interdisciplinary spirit of CIKM. 

Submission Guidelines

To facilitate the exchange of ideas, this year we adopt a policy similar to that of ICTIR'23 which allows submissions of any length between 2 and 9 pages plus unrestricted space for references. Authors are expected to submit a paper whose length reflects what is needed for the content of the work, i.e., page length should be commensurate with contribution size. 

All submissions must be in English, in ACM two-column format and made ready for the double-blind review. More details on the submission guidelines and templates can be found on the workshop website.

Submissions should be made electronically via EasyChair.

List of Topics

We encourage submissions on novel mining and learning based solutions in various aspects of legal data analysis such as legislations, litigations, court cases, contracts, patents, NDAs and bylaws. Topics of interest include, but are not limited to: 

• Applications of Large Language Models (LLMs) and Generative AI in the legal domain 
  • Prompt engineering and automated prompting for legal NLP tasks
  • LLMs for legal contract drafting 
  • Legal assistance using conversational AI  
  • Risks and limitations of LLMs in the legal domain 
• Applications of data mining techniques in the legal domain 
  • Classifying, clustering, and identifying anomalies in big corpora of legal records 
  • Legal analytics 
  • Citation analysis for case law 
• Applications of machine learning and NLP techniques for legal textual data 
  • Information extraction, information retrieval, question answering and entity extraction/resolution for legal document reviews 
  • Summarization of legal documents 
  • eDiscovery in legal research 
  • Case outcome prediction 
  • Legal language modelling and legal document embedding and representation 
  • Recommender systems for legal applications 
  • Topic modeling in large amounts of legal documents 
• Training data for the legal domain 
  • Acquisition, representation, indexing, storage, and management of legal data 
  • Automatic annotation and learning with human in the loop 
  • Data augmentation techniques for legal data 
  • Semi-supervised and transfer learning, domain adaptation, distant supervision 
• Ethical issues in mining legal data 
  • Privacy and GDPR in legal analytics 
  • Bias and trust in the applications of data mining
  • Transparency in legal data mining 
• Emerging topics in the intersection of AI and law
  • Digital lawyers and legal machines 
  • Smart contracts 
  • Future of law practice in the era of Generative AI

Committees

Program Committee

• Arian Askari, Leiden University, Netherlands
• Pan Du, Thomson Reuters Labs, Canada
• Shang Gao, Casetext, USA
• Shoaib Jameel, University of Southampton, UK
• Evangelos Kanoulas, University of Amsterdam, Netherlands
• Dave Lewis, Redgrave Data, USA
• Haiming Liu, University of Southampton, UK
• Yiqun Liu, Tsinghua University, China
• Miguel Martinez, Law Business Research, UK
• Isabelle Moulinier, Thomson Reuters Labs, USA
• Aileen Nielsen, Harvard University, USA
• Joel Niklaus, Standford University, USA
• Milda Norkute, Thomson Reuters Labs, Switzerland
• Douglas Oard, University of Maryland, USA
• Jaromir Savelka, Carnegie Mellon University, USA
• Frank Schilder, Thomson Reuters Labs, USA
• Shohreh Shaghaghian, Amazon, Canada
• Dietrich Trautmann, Thomson Reuters Labs, Switzerland
• Xiaoling Wang, East China Normal University, China
• Gineke Wiggers, Wolters Kluwer, Netherlands
• Josef Valvoda, University of Cambridge, UK
• Jun Xu, Renmin University, China
• Fattane Zarrinkalam, University of Guelph, Canada

Organizing committee

• Masoud Makrehchi, Thomson Reuters Labs & OntarioTech University, Canada
• Dell Zhang, Thomson Reuters Labs, UK
• Alina Petrova, Thomson Reuters Labs, UK
• John Armour, University of Oxford, UK

Publication

MLLD-2023 accepted papers will be published as non-archival proceedings on arXiv.org, similar to IPA'20. Thus, authors can refine their accepted papers and submit them to formal conferences/journals after the workshop.

Venue

The workshop will be held in conjunction with the 32nd ACM International Conference on Information and Knowledge Management (CIKM-2023).

It will take place on 22nd October 2023, in Birmingham, UK.

Contact

All questions about submissions should be emailed to Dell Zhang or Alina Petrova.