Harry Surden - Artificial Intelligence and Law Overview

System detects patterns in Email About likely markers of spam Detected Pattern Emails with "Earn Cash" More likely to be spam email Can use such detected patterns to make automated decisions about future emails Example: Email Spam Filter "Earn Cash" "Earn Cash" detected in 10% of Spam emails 0% of wanted emails Identification Improves Algorithm improves in performance In auto-identifying spam As it is able to examine more data And find additional indicia of spam Algorithm is "learning" over time from additional examples Example: Email Spam Filter "Free" Probability of Spam Contains "Free" 70% Spam Contains "Earn Cash" 90% Spam From Belarus 85% Spam For some (not all) complex tasks Requiring intelligence Intelligent Results Without Intelligence Can get "intelligent" automated results without intelligence By finding suitable Proxies or Patterns People use advanced cognitive skills to translate Proxies for Intelligent Results Without Intelligence Google finds statistical correlations by analyzing previously translated documents Statistical Machine Translation Produces automated translations using statistical likelihood as a "proxy" for underlying meaning Detecting Patterns Proxy Principle for Automation That can serve as Proxies For some underlying Cognitive Task Learning Machine Learning Main Points Pattern Detection Data Self-Programming Summary Major AI Approaches Two Major AI Techniques • Logic and Rules-Based Approach • Machine Learning (Pattern-Based Approach) Hybrid Systems • Many successful AI systems are hybrids of • Machine learning & Rules-Based Hybrids • e.g. Self-driving cars employ both approaches • Human intelligence AI Hybrids • Also, many successful AI systems work best when • They work with human intelligence • AI systems supply information for humans Humans Computers Technology Enhancing (Not Replacing) Humans Humans Alone Computers Alone Examples of AI in Law Today • Machine Learning • AI in Litigation - E-Discovery and "Predictive Coding" • Natural Language Processing (NLP) of Legal Documents • Automated contract analysis • Predictive Analytics for Litigation • Machine Learning Assisted Legal Research • Logic and Rules-Based Approaches • Compliance Engines • Expert Systems • Attorney Workflow Rule Systems • Automated Document Assembly Limits on Artificial Intelligence • Artificial Intelligence Accomplishments • Automate many things that couldn't do before • Limits • Many things still beyond the realm of AI • No thinking computers • No Abstract Reasoning • Often AI systems Have Accuracy Limits • Many things difficult to capture in data • Sometimes Hard to interpret Systems Questions Harry Surden Associate Professor of Law University of Colorado Law School Affiliated Faculty, Stanford CodeX Center Twitter: @HarrySurden Email: hsurden@colorado.edu