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Thoughts on Legal Prediction and Legal Metrics - Association of Corporate Counsel / Huron Consulting Meeting for Law Department Operations - Professor Daniel Martin Katz
This document discusses quantitative legal prediction and metrics that matter for law departments. It covers prediction being harder than backward-looking measures but implicit prediction occurring all the time. Two key ideas about prediction are the inverse problem of inducing a plausible model from existing data and validating it, and system dynamics viewing legal systems as complex/predictable versus chaotic. Concrete applications proposed are predicting case outcomes, better pricing with forensics, second generation predictive coding, mapping deal drafting to outcomes, and truly measuring attorney quality and performance.
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Thoughts on Legal Prediction and Legal Metrics - Association of Corporate Counsel / Huron Consulting Meeting for Law Department Operations - Professor Daniel Martin Katz
- 1. Thoughts on Legal Prediction and Legal Metrics Metricsthat Matter Law Department Operations Meeting @ computationalcomputationallegalstudies.comprofessor daniel martin katz Sponsored By: Association of Corporate Counsel Huron Legal Consulting
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- 5. The Age of QuantitativeLegal Prediction 2011
- 6. 2011 The Age of QuantitativeLegal Prediction
- 7. 2012 The Age of QuantitativeLegal Prediction
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- 9. Quantitative Legal Prediction - or - How I Learned to Stop Worrying and Start Preparing for the Data Driven Future of the Legal Services Industry Daniel Martin Katz Assistant Professor of Law Michigan State University 2013
- 10. The Science ofPrediction
- 11. Prediction is much harder thanbackward looking measures
- 12. Implicit Prediction is taking placeall of the time
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- 21. (1) Inverse Problem (2)System Dynamics
- 22. Hypothesis Testing is theCore of Mainstream Science
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- 28. In Case You Didnot Know
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- 30. This is theAge of Aspirational Spelling (Is Spelling 1.0 Thinking?)
- 32. (a) Induce aPlausible Model from Existing Data
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- 34. Either: Out of Sample ForwardPrediction Or Both
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- 40. vs. Complex/ Predictable Complex/ Chaotic TIDES ALMANAC
- 41. Some Formal Treatmentof the Science of Prediction
- 42. Big Data inLegal?
- 43. Road Map tothe Future
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- 48. Mapping of DealDrafting to Substantive Outcomes
- 49. How Do WeTruly Measure Attorney Quality + Performance?
- 50. Daniel Martin Katz IllinoisTech – Chicago Kent Law Associate Professor of Law @ computational computationallegalstudies.com danielmartinkatz.com lexpredict.com