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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:
-
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
-
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
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