-------- Forwarded Message -------- Subject: [AISWorld] [CFP] MLLD 2021: Final Call for Paper + Keynote Speaker Announcement Date: Tue, 10 Aug 2021 11:45:10 -0400 From: Shohreh Shaghaghian shaghaghian.shohreh@gmail.com To: aisworld@lists.aisnet.org
The 2nd International Workshop on Mining and Learning in the Legal Domain https://sites.google.com/view/mlld2021/home (MLLD 2021)
@ The IEEE International Conference on Data Mining (ICDM-2021 https://icdm2021.auckland.ac.nz/)
December 7-10, 2021 in Auckland, New Zealand
Important Dates:
Paper submission due date: *September 3, 2021*
Notification of acceptance: September 24, 2021
Camera ready deadline: October 1, 2021
Workshop date: December 7, 2021
Details:
The increasing accessibility of large legal corpora and databases create opportunities to develop data driven techniques as well as more advanced tools that can facilitate multiple tasks of researchers and practitioners in the legal domain. While recent advancements in the areas of data mining and machine learning have gained many applications in domains such as biomedical, healthcare and finance, there is still a noticeable gap in how much the state-of-the-art techniques are being incorporated in the legal domain. Achieving this goal entails building a multi-disciplinary community that can benefit from the competencies of both law and computer science experts. The goal of this workshop is to bring the researchers and practitioners of both disciplines together and provide an opportunity to share the latest novel research findings and innovative approaches in employing data analytics and machine learning in the legal domain.
Topics:
Topics of interest include, but are not limited to:
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Applications of data mining techniques in the legal domain -
case outcome prediction -
classifying, clustering and identifying anomalies in big corpora of legal records -
legal analytics -
citation analysis for case law -
eDiscovery
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Applications of natural language processing and machine learning techniques for legal textual data -
information extraction and entity extraction/resolution for legal document reviews -
information retrieval and question answering in applications such as identifying relevant case law -
summarization of legal documents -
legal language modelling and legal document embedding and representation -
recommender systems for legal applications -
topic modelling in large amounts of legal documents -
harnessing of deep learning approaches -
Ethical issues in mining legal data -
privacy and GDPR in legal analytics -
bias in the applications of data mining -
transparency in legal data mining -
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 learning, domain adaptation, distant supervision and transfer learning -
Emerging topics in the intersection of data mining and law -
digital lawyers and legal machines -
smart contracts -
future of law practice in the age of AI
Submissions:
You are invited to submit your original research and application papers to the workshop. As per ICDM instructions, papers are limited to a maximum of 8 pages (plus 2 extra pages if necessary) and must follow the IEEE ICDM format requirements. All accepted workshop papers will be published in the formal proceedings by the IEEE Computer Society Press. Each paper is reviewed by at least 3 reviewers from the program committee and manuscripts are to be submitted through CyberChair.
Keynote Speaker:
It is with great honor to announce that Dr. Sharad Goel of Harvard University will be giving the keynote talk at 2nd MLLD workshop on the topic of "*Designing Equitable Algorithms for Criminal Justice and Beyond*". Dr. Goel is a Professor of Public Policy at the Harvard Kennedy School. He looks at public policy through the lens of computer science, bringing a computational perspective to a diverse range of contemporary social and political issues, including criminal justice reform, democratic governance, and the equitable design of algorithms. More information about Dr. Goel research can be found on his website https://5harad.com/.
Thomson Reuters Labs Best Student Paper Award:
Thomson Reuters Labs https://www.thomsonreuters.com/en/artificial-intelligence.html will generously provide a total of $1000 USD to the best paper(s) submitted (one $1000 award or two $500 awards). The successful paper(s) must have at least one student author, and a student must be cited as the first author. The best paper recipient(s) will be selected by the program committee.
More information can be found on the workshop’s website https://sites.google.com/view/mlld2021/home.
Organizing Committee:
Masoud Makrehchi masoud.makrehchi@uoit.ca, OntarioTech University and Thomson Reuters Labs, Toronto, Canada
Shohreh Shaghaghian shohreh.shaghaghian@thomsonreuters.com, Thomson Reuters Labs, Toronto, Canada
Ali Vahdat Ali.Vahdat@thomsonreuters.com, Thomson Reuters Labs, Toronto, Canada
Fattane Zarrinkalam Fattane.Zarrinkalam@thomsonreuters.com, Thomson Reuters Labs, Toronto, Canada _______________________________________________ AISWorld mailing list AISWorld@lists.aisnet.org