Machine learning e o direito

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Mathias Winther Madsen, “The Limits of Machine Translation”, 5–15 (Dec. 23, 2009) (unpublished Master thesis, University of Copenhagen), disponível em http://www.math.ku.dk/ ~m01mwm/The%20Limits%20of%20Machine%20Translation%20%28Dec.%2023,%202009%29.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

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

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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).