![“[I]n 2001, a 300 Gb legal matter would take 200 attorneys a full
year to review, at a cost of about $15 million.
In 2003, a similar-sized matter took 100 attorneys 3 weeks to
complete, at a cost of $6 million.
And in 2006, a 300 Gb investigation took 65 attorneys only 2.5
days to complete, at a cost of $2 million.
And now, cases with several hundreds of Gbs are routine.”
Improving Document Review in E-Discovery
FTI Consulting](https://image.slidesharecdn.com/ediscoveryin2015-130130155258-phpapp02/75/Predictive-Coding-and-E-Discovery-in-2015-and-Beyond-LegalTechNYC-2013-Daniel-Martin-Katz-Michael-J-Bommarito-II-62-2048.jpg)
Uploaded byDaniel Katz
Predictive Coding and E-Discovery in 2015 and Beyond - LegalTechNYC 2013 ( Daniel Martin Katz + Michael J. Bommarito II )
This document discusses predictive coding and machine learning methods for e-discovery. It begins by explaining that predictive coding relies on supervised machine learning methods, which use human-coded training sets to classify documents as relevant or non-relevant. The document then discusses different types of supervised and unsupervised machine learning algorithms that could be used for predictive coding. It suggests that using prior cases to inform feature selection and weightings could make algorithms "smarter." Finally, it considers whether discovery costs will eventually be reduced due to scaling relationships for cost per gigabyte and long-term rates of electronic data growth.
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Predictive Coding and E-Discovery in 2015 and Beyond - LegalTechNYC 2013 ( Daniel Martin Katz + Michael J. Bommarito II )
- 1. @ computationalcomputationallegalstudies.com Predictive Coding andE-Discovery in 2015 and Beyond daniel martin katz michael j bommarito ii
- 2. The 2x2 MachineLearning Spectrum Info Viz & Pattern Detection Rates of Scaling
- 3. The 2x2 Machine LearningSpectrum
- 4. In Order to UnderstandWhere We Are Heading ...
- 5.
- 6. it is necessaryto have insight regarding how predictive coding actually works
- 7. Predictive Coding Relies Upona Particular Class of Machine Learning Methods
- 8. Predictive Coding Relies Upona Particular Class of Machine Learning Methods
- 9. The Current Approach isdrawn from the family of so called “supervised methods”
- 10. What is thedifference between supervised and unsupervised?
- 12. As you havelikely seen ...
- 13.
- 14. Develop a TrainingSet using human experts
- 15. In the simplecase, assign objects to two piles
- 16. Take This DocumentSet ...
- 17.
- 18. yellow = relevant white= non-relevant And Return This ...
- 19.
- 20.
- 21. What Allows AHuman To Separate These Two Classes of Documents?
- 22. that precise human processis what predictive coding is trying to mimic
- 23. Humans are selecting uponfeatures of documents
- 24. to place those documentsin their respective bins (i.e. relevant, non-relevant)
- 25.
- 26. supervised methods “learn” fromthe training data
- 27. but there aredifferent forms of learning by machines ...
- 28. There Is Learning Withina Matter (i.e. learning from a specific training set)
- 29. But what aboutusing prior matters to inform both feature selection and the weighting of those features
- 30. In other words,it is possible to learn from the experience of having processed documents in the past
- 31. both inside agiven company but also across companies ...
- 32. It comes from dataaggregation / reusing data
- 33. This is Learningand Rule Propagation Across Matters
- 34. feedback loops arethe best friends of algorithms
- 35. feedback loops canhelp make algorithms become much smarter ...
- 37. Supervised Unsupervised Predictive Coding The Future Machine Learning Methods 2x 2 Informed Naive Basic Clustering Algorithm
- 38. Supervised Statistical models Bayesian, e.g.,Naïve Bayes Classification Frequentist, e.g., Ordinary Least Squares Neural Networks (NN) Support Vector Machines (SVM) Random Forests (RF) Genetic Algorithms (GA) Semi/unsupervised Neural Networks (NN) Clustering K-means Hierarchical Radial Basis (RBF) Graph Some Machine Learning Algorithms
- 40.
- 41. Think about thetask faced by the intelligence community ...
- 42. mountains of information toprocess
- 43.
- 44.
- 45. The Visual Cortexis a very powerful CPU ...
- 46. We are verygood pattern detectors ...
- 47. We need amix of analytics and viz ...
- 49. because there aresignificant efficiency gains to be obtained from applications of sophisticated data visualization techniques
- 50. This Next Generationof EDiscovery Software is viz intensive ...
- 52. but this isonly the beginning ...
- 55. including an evenmore enriched notion of time dynamics ...
- 58.
- 59. Will Discovery Costs EventuallyBe Reduced?
- 60. Two Scaling Relationships thatare in question ...
- 61.
- 62. “[I]n 2001, a300 Gb legal matter would take 200 attorneys a full year to review, at a cost of about $15 million. In 2003, a similar-sized matter took 100 attorneys 3 weeks to complete, at a cost of $6 million. And in 2006, a 300 Gb investigation took 65 attorneys only 2.5 days to complete, at a cost of $2 million. And now, cases with several hundreds of Gbs are routine.” Improving Document Review in E-Discovery FTI Consulting
- 63. Past Rate of ESICreation
- 64. Long Term Rate ofESI Creation ?
- 65. Daniel Martin Katz MichiganState University Associate Professor of Law @ computational computationallegalstudies.com reinventlaw.com http://about.me/daniel.martin.katz