[CFP] The 3rd International Workshop on Mining and Learning in the Legal Domain (MLLD@CIKM-2023)

The 3rd International Workshop on Mining and Learning in the Legal Domain (MLLD)
In conjunction with the 32nd ACM International Conference on Information and Knowledge Management (CIKM-2023)
University of Birmingham and Eastside Rooms, UK
Sunday 22nd October 2023

# Important Dates

- Paper submission deadline: August 18th, 2023
- Paper acceptance notification: September 15th, 2023
- Paper final version due: October 1st, 2023
- Workshop date: October 22nd, 2023

# Abstract

The increasing accessibility of legal corpora and databases create opportunities to develop data-driven techniques and advanced tools that can facilitate 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. 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. The MLLD workshop aims to bring together researchers and practitioners to share the latest research findings and innovative approaches in employing data mining, machine learning, information retrieval, and knowledge management techniques to transform the legal sector. Building upon the previous successes, the third edition of the MLLD workshop will emphasize the exploration of new research opportunities brought about by recent rapid advances in 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.

# 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 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

# Website

https://sites.google.com/view/mlld2023/

########################################################################

To unsubscribe from the IR list, click the following link:
https://www.jiscmail.ac.uk/cgi-bin/WA-JISC.exe?SUBED1=IR&A=1

This message was issued to members of www.jiscmail.ac.uk/IR, a mailing list hosted by www.jiscmail.ac.uk, terms & conditions are available at https://www.jiscmail.ac.uk/policyandsecurity/