Mandating Use of Predictive Coding in Electronic Discovery: An Ill‐Advised Judicial Intrusion

Murphy, Tonia Hap

doi:10.1111/ablj.12015

Cited by 5 publications

(1 citation statement)

References 9 publications

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“…The improvements in the machine learning part of predictive coding (applying Continuous Active Learning protocols) have been made by combining the training and review into one stage, allowing the software algorithm to improve as long as the review process is ongoing (Tredennick et al, 2015;Losey,2015). Therefore it contributes significantly to cutting costs and minimising the time-consuming aspects of the review and analysis process (Murphy, 2013) However, the power of such underlying supervised learning algorithms in ranking documents is limited by the reviewer's knowledge of the case. Any developments in the reviewer's understanding of the documents relevant to the case are reflected back to the machine algorithm (Tredennick et al, 2015), affecting the machine model's decisions in classifying documents.…”

Section: Context and Backgroundmentioning

confidence: 99%

E-Discovery: Towards a Narrative Representation Approach to Enhance Predictive Coding-based Review

Alsufiani

2016

Electronic Workshops in Computing

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In the evidential reasoning of human beings, stories play a role in providing an analytic account of the facts. In a legal aspect, the approach of legal reasoning, based on the notion of narratives with evidence, needs to be investigated and supported. The study aims to understand how lawyers cognitively process e-discovery cases, in order to find out the use of narrative in the e-discovery of legal reviews and the analysis process, to build a software tool to assist them.

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Section: Context and Backgroundmentioning

confidence: 99%