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Open Legal Data Workshop at Stanford

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On May 19, 2016 I hosted a workshop at Stanford's Codex Center about ways to make legal data more open and accessible for computation. These are the slides from my presentation framing the issue.

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Open Legal Data Workshop at Stanford

  1. 1. Open Legal Data Workshop Stanford University CodeX Center Harry Surden Professor of Law, University of Colorado Affiliated Faculty: Stanford CodeX Center
  2. 2. Overview • Computation has Revolutionized Many Fields • Law is not one of them • Data is required for Computational Analysis • Legal data: neither accessible nor high-quality
  3. 3. Computational Legal Analysis • Computational Law (Rules-based, deductive) – Rules-based systems computing legal outcomes – Represent laws in computer-understandable form – Example: Turbotax; Computable contracts • Machine Learning & Law (often Statistical) – Algorithms that learn patterns from data – Widely Used: self-driving cars, translation, etc – Example: Supreme Court prediction project
  4. 4. Problem • Computation has revolutionized: – Finance, medicine, engineering, science, etc. – Machine learning and computation used for • Prediction, automation, outlier detection, analysis, • New drug discovery, etc • But computation has barely touched law – Why?
  5. 5. To do computation We need data to analyze • Think of Law as data to be analyzed – Federal statutes and administrative rules – State and local laws and codes – Judicial orders and opinions – Lawsuit motions and evidence, etc. Quality legal data not widely available for analysis
  6. 6. The Legal Data Bottleneck • Legal data exists, but it is not – Openly accessible (behind pay-walls) – Structured in a way that makes analysis feasible • Lack of widely accessible legal data – Bottleneck to really interesting work in • Machine learning and Law • Computational law
  7. 7. For really interesting computational work in law we need • High-quality legal data that is – Open and Accessible (little or no cost) – Structured (machine readable) – Standardized (common encoding formats) – Coded (human-tagged and organized) – Semantic (embedded with meaning)
  8. 8. Possibilities harrysurden.com
  9. 9. Possibilities lexpredict.com
  10. 10. Possibilities • With high quality, structured legal data: – Predictions of federal, state court decision – Finding patterns or biases in legal data – More computational law systems – Advanced legal data visualizations – Discovery of unknown connections or structures – Outlier detection – ….many more
  11. 11. Open Legal Data • Legal data for computation that is: – Open and Accessible (little or no cost) – Structured (machine readable) – Standardized (common encoding formats) – Coded (human-tagged and organized) – Semantic (embedded with meaning)

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