Congrats to Hon. Ann Aiken and the other 2014 ABA Journal Legal Rebels

Congrats to my good friend – Judge Ann Aiken and the other 2014 Legal Rebels!  See her talk from ReInventLaw Silicon Valley 2013 here.

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Author Mentoring Program @ 15th International Conference on Artificial Intelligence & Law (ICAIL 2015)

ICAIL_MentorshipProgramThe International Association of Artificial Intelligence and Law (IAAIL) is offering a mentoring program for papers being submitted to its biennial ICAIL conference, the International Conference on Artificial Intelligence and Law. The program is intended primarily for junior authors who have not previously published an Artificial Intelligence and Law paper at a conference or in a journal. If you would like help with your submission, you may ask for a mentor ― a person who will help you with your submission to the IAAIL audience through one-on-one advising, usually via e-mail. A mentor can also familiarize you with the standards and deadlines of ICAIL submissions. Mentors are volunteers familiar with successful submissions. To request a mentor, please contact us by the Mentoring Program Request Deadline.

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U.S. Digital Services Playbook (via cio.gov)

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Biological Inspired Computing: Programmable Self-Assembly in a Thousand-Robot Swarm (via Wyss Institute @ Harvard)

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Artificial Intelligence, Robotics and the Future of Jobs (via Pew Research)

Pew_FutureofWork

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This Computer Program Can Predict 7 out of 10 Supreme Court Decisions (via Vox.com)

scotus_predictionThe story is here.  Full form interview with Mike + Josh is here. (I unfortunately could not participate because I was teaching my ICPSR class).  Our paper is available on SSRN and on the physics arXiv.

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Vax! A Game About Epidemic Prevention

Vax_Game

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This is Law School? Socrates Takes a Back Seat to Business and Tech (via New York Times)

ThisIsLawSchoolNice coverage of our efforts at MSU Law to inject our students with important skills that can be competitive differentiator in this difficult legal marketplace.  As Dan Rodriguez described it – one sweet spot for differentiation is located somewhere in and around “the law/business/technology interface.” I completely agree.  While it is far from the only mission, there is arbitrage located in this sweet spot because many law schools do not have faculty with the appropriate technical skills necessary to teach in this space (see also a lack of desire/vision).  This creates room for others.  I outlined all of this in some detail in my Keynote Address at the Stanford CodeX Conference last year (and in the forthcoming paper called “The MIT School of Law“).  As MSU Law Dean Joan Howarth said “[L]egal education has been stronger on tradition than innovation …. What we’re trying to do is educate lawyers for the future, not the past.” Well said!  I joined the faculty at MSU three years ago with the goal doing the very things that are now up and running – however – there is always more to do – so stay tuned for more.

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

SCOTUS Prediction Model
Abstract
:  “Building upon developments in theoretical and applied machine learning, as well as the efforts of various scholars including Guimera and Sales-Pardo (2011), Ruger et al. (2004), and Martin et al. (2004), we construct a model designed to predict the voting behavior of the Supreme Court of the United States. Using the extremely randomized tree method first proposed in Geurts, et al. (2006), a method similar to the random forest approach developed in Breiman (2001), as well as novel feature engineering, we predict more than sixty years of decisions by the Supreme Court of the United States (1953-2013). Using only data available prior to the date of decision, our model correctly identifies 69.7% of the Court’s overall affirm and reverse decisions and correctly forecasts 70.9% of the votes of individual justices across 7,700 cases and more than 68,000 justice votes. Our performance is consistent with the general level of prediction offered by prior scholars. However, our model is distinctive as it is the first robust, generalized, and fully predictive model of Supreme Court voting behavior offered to date. Our model predicts six decades of behavior of thirty Justices appointed by thirteen Presidents. With a more sound methodological foundation, our results represent a major advance for the science of quantitative legal prediction and portend a range of other potential applications, such as those described in Katz (2013).”

You can access the current draft of the paper via SSRN or via the physics arXiv.  Full code is publicly available on Github.  See also the LexPredict site.  More on this to come soon …

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Dan Lear – We Need More Legal Hackers Now (via ReInvent Law Channel.com)

Dan Lear – We Need More Legal Hackers Now from ReInvent Law Channel on Vimeo.

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ZoningCheck Puts Computable Municipal Codes to Use (via TechRepublic)

ComputationalMunicipalCodesHT: Dazza Greenwood from MIT Media Lab

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Visualizing Criminal Networks Reconstructed from Mobile Phone Records

cellphonenetworks

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Watch London in Real Time (via We Are The Data)

wearedata

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Basha Rubin – Everyone is an Expert: Lawyering in the Age of Self-Diagnosis (via ReInventLawChannel.com)

Basha Rubin – Everyone is an Expert: Lawyering in the Age of Self-Diagnosis from ReInvent Law Channel on Vimeo.

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