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.
Difference Between Search & Browse Methods in Odoo 17
Open Legal Data Workshop at Stanford
1. Open Legal Data
Workshop
Stanford University CodeX Center
Harry Surden
Professor of Law, University of Colorado
Affiliated Faculty: Stanford CodeX Center
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. 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. 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. 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. 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. 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)
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. 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)