With the increased focus within the discipline of eDiscovery on Technology-Assisted Review, three references are provided to help legal professionals establish a solid base of definitional and contextual information for considering machine learning.
Reference #1: Book: New Advances in Machine Learning. Chapter: Types of Machine Learning Algorithms. Author: Taiwo Oldipupo Ayodele (University of Portsmouth, United Kingdom).
Reference #2: Video: Lectures on Machine Learning. Lecturer: Andrew Nq (Director , Stanford Artificial Intelligence Lab, Stanford University).
Available via Video Series Link: https://class.coursera.org/ml/lecture/preview
Reference #3: Video Lectures on Machine Learning. Lecturer: Pedros Domingos (Professor of Computer Science & Engineering, University of Washington.
Available via Video Series Link: https://class.coursera.org/machlearning-001/lecture/preview
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Based on an informal review of research from technology providers, industry analyst firms, and industry expert reports in the data discovery arena, the following short list of enablers highlights companies and technologies that may be useful to technology providers as legal discovery professionals seek to move “to the left of the EDRM” and closer to the point of data creation in their data discovery efforts.
In 2017, the challenge for technology providers in the legal and data discovery spaces appears to be less about defining offering requirements and validating market needs and more about developing and delivering solutions that focus on specific tasks and processes that streamline the discovery of data and the conduct of eDiscovery.
The Victorian Supreme Court will issue a practice note about the use of TAR on 1 January 2017. We understand it will be the first court in Australia to do so. We expect that other Australian courts will follow suit in issuing a practice note, and it will be interesting to follow the approaches taken by other Australian courts.
The biggest takeaway of the joint research project by nonprofit Electronic Discovery Institute and tech giant Oracle Corp. is that TAR is often faster and cheaper when identifying relevant documents. But when it comes to isolating privileged or sensitive information, human reviewers outperformed machines.
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