Machine learning and law

Administrative Office of the U.S. Courts, 25 Years Later, PACER, Electronic Filing Continue to Change Courts, THE THIRD BRANCH NEWS (Dec. 9, 2013), http://news.uscourts.gov/25- years-later-pacer-electronic-filing-continue-change-courts

Amanda Conley et al., “Sustaining Privacy and Open Justice in the Transition to Online Court Records: A Multidisciplinary Inquiry”, 71 Md. L. Rev. 772 (2012)

Andrew D. Martin et al., “Competing Approaches to Predicting Supreme Court Decision Making”, 2 Persp. On Pol. 761, (2004)

Argye E. Hillis & Alfonso Caramazza, “The Reading Process and Its Disorders”, in COGNITIVE NEUROPSYCHOLOGY IN CLINICAL PRACTICE 229, (David Ira Margolin ed., 1992)

Brian Z. Tamanaha, “Understanding Legal Realism”, 87 Tex. L. Rev. 731, (2009)

Burkhard Bilger, “Auto Correct: Has the Self-Driving Car at Last Arrived?”, NEW YORKER, Nov. 25, 2013

Cass R. Sunstein, Kevin Ashley, Karl Branting & Howard Margolis, “Symposium: Legal Reasoning and Artificial Intelligence: How Computers Think Like Lawyers”, 8 University of Chicago Law School Roundtable 1 (2001)

Christopher D. Manning, “Introduction to information retrieval”, (2008)

Daniel Martin Katz, “Quantitative Legal Prediction—or—How I Learned to Stop Worrying and Start Preparing for the Data-Driven Future of the Legal Services Industry”, 62 Emory L.J. 909 (2013)

David Barber, “Bayesian reasoning and machine learning”, (2011)

David Bellos, “Is that a fish in your ear? Translation and the meaning of everything”, (2011)

David E. Sorkin, “Technical and Legal Approaches to Unsolicited Electronic Mail”, 35 U.S.F. L. Rev. 325 (2001)

David L. Schwartz, “Practice Makes Perfect? An Empirical Study of Claim Construction Reversal Rates in Patent Cases”, 107 Mich. L. Rev. 223 (2008)

Ekaterina Ovchinnikova, “Integration of world knowledge for natural language understanding”, (2012)

ENCYCLOPEDIA OF MACHINE LEARNING, (Claude Sammut & Geoffrey I. Webb eds., 2011)Find Out How Our Translations Are Created, GOOGLE, http://translate.google.com/about (ultimo acesso: Feb. 24, 2014)

H. C. Tijms, “Understanding probability”, (3d ed. 2012)Harry Surden, “Computable Contracts”, 46 U.C. Davis L. Rev. 629 (2012)

I. H. Witten, “Data mining: practical machine learning tools and techniques”, (3d ed. 2011)

I. H. Witten, “Data mining: practical machine learning tools and techniques”, (2d ed. 2005)

Janice M. Mueller, “Patent Law”, (4th ed. 2012)

John Markoff, “Armies of Expensive Lawyers, Replaced by Cheaper Software”, N. Y. TIMES (Mar. 4, 2011), http://www.nytimes.com/2011/03/05/science/05legal.html

Jonathan R. Macey, “Promoting Public-Regarding Legislation Through Statutory Interpretation: An Interest Group Model”, 86 Colum. L. Rev. 223, (1986)

Karl Okamoto, “Teaching Transactional Lawyering”, 1 Drexel L. Rev. 69 (2009)

Kevin D. Ashley & Stefanie Brüninghaus, “Automatically Classifying Case Texts and Predicting Outcomes”, 17 Artificial Intelligence & L. 125, 125–65 (2009)

Lawrence Maisel, “Predictive business analytics: forward looking capabilities to improve business performance”, (2014)

Mathias Winther Madsen, “The Limits of Machine Translation”, 5–15 (Dec. 23, 2009) (unpublished Master thesis, University of Copenhagen), disponível em http://www.math.ku.dk/ ~m01mwm/The%20Limits%20of%20Machine%20Translation%20%28Dec.%2023,%202009%29. pdf.

Mehryar Mohri Et Al., “Foundations of machine learning”, (2012)

Nassim Nicholas Taleb, “The black swan: the impact of the highly improbable”, (2d ed. 2010)

Parag Kulkarni, “Reinforcement and systemic machine learning for decision making 1–2”, (2012)

Patrick E. Longan, “The Shot Clock Comes to Trial: Time Limits for Federal Civil Trials”, 35 Ariz. L. Rev. 663, (1993)

Paul Brest & Linda Hamilton Krieger, “Problem solving, decision making and professional judgment”, (2010)

Paul Ducklin, “Dirty Dozen Spam Sending Nations”, NAKED SECURITY (Oct. 17, 2013), http://nakedsecurity.sophos.com/2013/10/17/dirty-dozen-spam-sendingnations-find-where-you- finished-in-our-q3-spampionship-chart/.

Pedro Domingos, “A Few Useful Things to Know About Machine Learning”, COMM. ACM, Oct. 2012

Peter Flach, “Machine Learning: The art and science of algorithms that make sense of data 3”, (2012)

Richard Socher et al., “Semantic Compositionality through Recursive MatrixVector Spaces”, in CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING, (2012)

Robert Dale, “Classical Approaches to Natural Language Processing”, in HANDBOOK OF NATURAL LANGUAGE PROCESSING 1, 1–7 (Nitin Indurkhya & Frederick J. Damerau eds., 2d ed. 2010)

Rui Xu & Don Wunsch, “Clustering”, (2008)

Stanford IP Litigation Clearinghouse, STAN. L. SCH., http://www.law.stanford.edu/ organizations/programs-and-centers/stanford-ip-litigation-clearinghouse (ultimo acesso Jan. 27, 2014)

Stephen Marsland, “Machine learning: an algorithmic perspective”, (2011)

Stuart Russell & Peter Norvig, “Artificial intelligence: a modern approach”, (3d ed. 2010)

Tanina Rostain, “Ethics Lost: Limitations of Current Approaches to Lawyer Regulation”, 71 S. Cal. L. Rev. 1273, (1998)

Theodore W. Ruger et al., “The Supreme Court Forecasting Project: Legal and Political Science Approaches to Predicting Supreme Court Decision-Making”, 104 Colum. L. Rev. 1150, (2004)

Toby Segaran, “Programming collective intelligence: building smart web 2.0 applications 3”, (2007)

Tom Mitchell, “The discipline of machine learning”, Report No. Ml-06-Cmu-108 § 1 (2006), available at http://www.cs.cmu.edu/~tom/pubs/MachineLearning.pd

Vincent Syracuse et al., “E-Discovery: Effects of Automated Technologies on Electronic Document Preservation and Review Obligations”, INSIDE COUNSEL (Dec. 18, 2012), ht t p://m .insidecounsel.com/2012/12/18/e-discovery-effects-of-automated-technologies-on-e.

William S. Yerazunis, “The spam-filtering accuracy plateau at 99.9 percent accuracy and how to get past it”, (Dec. 2004), available at http://www.merl.com/ reports/docs/TR2004-091.pdf (noting that many spam filters have achieved accuracy rates at over 99.9%).

U.S.C. § 102(a) (2006 & Supp. V 2011).