Tag Archives: Quantitative legal prediction

Katz, Bommarito, and Blackman: Predicting the Behavior of the Supreme Court of the United States: A General Approach

Daniel Martin Katz, Michael Bommarito, and Josh Blackman have posted a working paper entitled Predicting the Behavior of the Supreme Court of the United States: A General Approach. Here is the abstract: Building upon developments in theoretical and applied machine … Continue reading

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Stanford Symposium on Law and Rationality: Legal Trial, With and Without Mathematics, 30 May 2014: Links and resources

The 2014 Stanford Symposium on Law and Rationality, on the theme: Trial With and Without Mathematics: Legal, Philosophical, and Computational Perspectives, is scheduled to be held 30 May 2014, at Stanford Law School, in Stanford, California, USA. The conference is … Continue reading

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McGinnis and Pearce: The Great Disruption: How Machine Intelligence Will Transform the Role of Lawyers in the Delivery of Legal Services

Professor John O. McGinnis of Northwestern University and Professor Russell G. Pearce of Fordham University have published The Great Disruption: How Machine Intelligence Will Transform the Role of Lawyers in the Delivery of Legal Services, Fordham Law Review, 82, 3041-3066 … Continue reading

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Chou et al.: Automated prediction of construction contract disputes with hybrid AI model

Professor Dr. Jui-Sheng Chou, Professor Dr. Min-Yuan Cheng, Yu-Wei Wu, and Anh-Duc Pham have published Optimizing parameters of support vector machine using fast messy genetic algorithm for dispute classification, Expert Systems with Applications, 41, 3955–3964 (2014). Here is the abstract: … Continue reading

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Conti et al.: A Conservation Theory of Governance for Automated Law Enforcement

Professor Dr. Gregory Conti, Professor Dr. Woodrow Hartzog, Professor Dr. John Nelson, and Professor Dr. Lisa A. Shay have posted the full text of their paper entitled A Conservation Theory of Governance for Automated Law Enforcement, presented this week at … Continue reading

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Surden: Machine Learning and Law

Professor Harry Surden of the University of Colorado has published Machine Learning and Law, Washington Law Review, 89, 87-115 (2014). Here is the abstract: This Article explores the application of machine learning techniques within the practice of law. Broadly speaking … Continue reading

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Ferguson: Big Data and Predictive Reasonable Suspicion

Professor Andrew Guthrie Ferguson of the University of the District of Columbia has posted Big Data and Predictive Reasonable Suspicion, forthcoming in University of Pennsylvania Law Review. Here is the abstract: The Fourth Amendment [to the U.S. Constitution] requires “reasonable … Continue reading

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Parker-Stephen and Zorn: Predictive Analytics for Substantive and Operational Decisions in Law

Dr. Evan Parker-Stephen and Professor Dr. Christopher Zorn, both of Lawyer Metrics LLC, have posted slides of their presentation entitled Predictive Analytics for Substantive and Operational Decisions, given 26 February 2014 at the Forum on Legal Evolution, in New York … Continue reading

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Katz and Bommarito: Legal Analytics, Machine Learning, and Comments on the Status of Innovation in the Legal Industry

Professor Dr. Daniel Martin Katz of Michigan State University and the ReInvent Law Lab, and Michael J. Bommarito II, M.S.E., M.A., of Bommarito Consulting, have posted slides of their presentation entitled Legal Analytics, Machine Learning, and Some Comments on the … Continue reading

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Forum on Legal Evolution, NYC, 26 February 2014: Storify, tweets, and resources

The Forum on Legal Evolution was held 26 February 2014 in New York City. According to Ron Friedmann, the event was organized by Professor William Henderson, Professor Dr. Daniel Martin Katz, and Bruce MacEwen. The Twitter hashtag for the event … Continue reading

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