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AI-Enabled Litigation Evaluation: Data-Driven Empowerment for Legal Decision Makers

Published: 17 June 2019 Publication History

Abstract

Lawsuits are expensive, time consuming, and often confusing to non-lawyers who are faced with making important legal decisions. For making informed legal decisions, as it stands today, there is a dearth of tools to aid legal decision makers in the costly and complex landscape of litigation. To address this need, we present a framework for standardizing and structuring key information contained in case dockets. This data can be used for numerous analytics use cases to provide legal decision makers with data-driven insights to help them understand and navigate the events that occur in their lawsuits.
The remainder of this document is organized as follows: In Section 1, we discuss the task of docket standardization, i.e., the process of defining a set of categories for classifying case docket entries. In Section 2, we present our iterative human-in-the-loop (HIL) approach to training machine learning (ML) models for categorizing docket entries at scale. In Section 3, we present some of the key features of LitiLens, which is a prototype software application developed by Komply for AI-enabled litigation evaluation. Finally, we propose elements of our work to demonstrate at the June 2019 ICAIL Conference in Section 4.

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  • (2021)A Review on Human–AI Interaction in Machine Learning and Insights for Medical ApplicationsInternational Journal of Environmental Research and Public Health10.3390/ijerph1804212118:4(2121)Online publication date: 22-Feb-2021
  1. AI-Enabled Litigation Evaluation: Data-Driven Empowerment for Legal Decision Makers

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    cover image ACM Conferences
    ICAIL '19: Proceedings of the Seventeenth International Conference on Artificial Intelligence and Law
    June 2019
    312 pages
    ISBN:9781450367547
    DOI:10.1145/3322640
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    • Univ. of Montreal: University of Montreal
    • AAAI
    • IAAIL: Intl Asso for Artifical Intel & Law

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 17 June 2019

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    • (2021)A Review on Human–AI Interaction in Machine Learning and Insights for Medical ApplicationsInternational Journal of Environmental Research and Public Health10.3390/ijerph1804212118:4(2121)Online publication date: 22-Feb-2021

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