Artificial Intelligence, Small Data, and Data-Driven Decisions to Improve Legal-Service Delivery
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Presentation at the Canadian Corporate Counsel Association National Conference in Toronto on April 4, 2017. Discussed relevant artificial intelligence methods (focusing on expert systems and machine learning), improving legal predictions by collecting small data, leveraging third-party data, and using decision trees, and a systematic framework for improving risk assessment.
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Artificial Intelligence, Small Data, and Data-Driven Decisions to Improve Legal-Service Delivery
- 1. @DanLinna @LegalRnD Artificial Intelligence, Small Data, and Data-Driven Decisions – The Industry Reloaded: 4.0 Skills for the 4.0 Future Canadian Corporate Counsel Association National Conference Toronto – April 4, 2017 Daniel W. Linna Jr. Professor of Law in Residence Director of LegalRnD – The Center for Legal Services Innovation
- 6. @DanLinna @LegalRnD How is the legal industry really changing? • People • Process • Technology
- 7. @DanLinna @LegalRnD People • Law very people focused – Lawyers deliver value – Opportunities for others to deliver value • Very little thought given to – Process – Technology
- 8. @DanLinna @LegalRnD Process & Technology • Disaggregate legal work – Discrete tasks and processes • Who does the work? – Make or buy? – Outsource? – Automate? • Development of legal supply chain
- 10. @DanLinna @LegalRnD 21st Century Legal Services • Business of law (e.g., legal operations) • Process improvement • Project management • Knowledge management • Metrics • Data analytics • Technology
- 11. @DanLinna @LegalRnD Data • “Big Data” and Technology – Automation • Expert Systems and document automation • Artificial Intelligence (e.g., Machine Learning) – Data Analytics (aka, prediction) • Litigation, Compliance, Transactions, etc. • Data-driven decision making – Metrics • Benchmarks • Efficiency • Quality – Small data
- 12. @DanLinna @LegalRnD Prediction • Experts • Crowds • Algorithms
- 13. @DanLinna @LegalRnD Algorithm “a step-by-step procedure for solving a problem or accomplishing some end especially by a computer” – Merriam-Webster Image source: Wikipedia at https://en.wikipedia.org/wiki/Algorithm
- 14. @DanLinna @LegalRnD Expert Systems (Rules-Based AI) Source: TutorialsPoint https://www.tutorialspoint.com/artificial_intelligence/artificial_intelligence_expert_systems.htm
- 19. @DanLinna @LegalRnD AI – Machine Learning Categorical Continuous Supervised Classification Regression Unsupervised Clustering Dimension Reduction
- 23. @DanLinna @LegalRnD AI - Machine Learning Applications • Technology Assisted Review –eDiscovery & Diligence • Compliance • Quantitative Legal Prediction – Litigation – Transactions
- 24. @DanLinna @LegalRnD TAR 1.0 Source: Catalyst TAR for Smart People at https://catalystsecure.com/tarforsmartpeople
- 25. @DanLinna @LegalRnD TAR 2.0 Source: Catalyst TAR for Smart People at https://catalystsecure.com/tarforsmartpeople
- 27. @DanLinna @LegalRnD Predicting a Lawyer’s Rate • Firm Size • Location • Partner Status • Experience • Practice Area • Regression?
- 31. @DanLinna @LegalRnD Can We Predict Case Outcomes?
- 33. @DanLinna @LegalRnD Before “Big Data,” Small Data • Create a data-driven culture – Establish Metrics – Capture Data (Do you have a data plan?) – Keep Score • Setting the stage for quantitative prediction and Artificial Intelligence
- 34. @DanLinna @LegalRnD Asking, for each decision: 1. What do we want to accomplish? 2. What data do we have? 3. What other sources of data can we use? 4. What action do we take?
- 35. @DanLinna @LegalRnD Litigation Outcomes • Systematically predict outcomes and costs • Communicate with client early and often • Use visual tools • Use external data sources • Keep score (i.e., compare predictions to actual outcomes) 35
- 36. @DanLinna @LegalRnD Decision Trees • Systematic approach • Identify uncertainty—legal and fact issues • Conduct sensitivity analysis • “Level set” lawyers and clients
- 39. @DanLinna @LegalRnD Litigation “Small Data” Examples • Matter type • Claim type • Jurisdiction • Judge • Lead lawyer • Exposure • Expected value at Early Case Assessment • Fees estimated at Early Case Assessment • Settlement amount • Days to decision (Motion to Dismiss, Summary Judgment, Trial) • Total fees
- 40. @DanLinna @LegalRnD Example: Basic Litigation Metrics • Keep score: predicted versus actual –Total cost to prosecute or defend –Amount to resolve –Duration
- 41. @DanLinna @LegalRnD Prediction • Experts • Crowds • Algorithms
- 42. @DanLinna @LegalRnD Transactions - Risk Assessment • Identification • Estimation (likelihood and value) • Evaluation • Treatment Mahler, Tobias, Tool-supported Legal Risk Management: A Roadmap, European Journal of Legal Studies (2010), available at: http://cadmus.eui.eu/handle/1814/15122
- 43. @DanLinna @LegalRnD Contract Risk Assessment • Specify scope and target – Client objectives? – How will risk be documented and measured? – Criteria for risk evaluation? • Risk identification – For each clause: • How will subsequent facts and decisions negatively affect objectives? • Risk estimation and evaluation – likelihood and value • Risk treatment and cost-benefit analysis
- 44. @DanLinna @LegalRnD Estimation & Evaluation Source: Just Get PMP http://www.justgetpmp.com/2012/02/probability-and-impact-matrix.html
- 45. @DanLinna @LegalRnD Data – Improving Legal-Service Delivery • Types of matters • Tasks • Timekeepers • Rates • Total fees • What about outcomes? Quality?
- 47. @DanLinna @LegalRnD Example: Lawyer-Quality Metric • On Time and Complete • Assignments – On time? – Complete?
- 48. @DanLinna @LegalRnD Example: Process Improvement Project • Measure – Current state – Future state • How do you measure value (ROI)? – Efficiency? – Quality? – Time? – Cost? • How do you value resources? – External resources – Internal resources – Opportunity costs
- 49. @DanLinna @LegalRnD NetApp NDA Example • Current state – Turnaround time: 5 days • Future state – Turnaround time: about an hour – Savings • 300,000 internal hours • $2 million in external costs Connie Brenton and D. Casey Flaherty, The data deficit: Proper measurement is a prerequisite to proper management, Inside Counsel, (November 1, 2015), available at: http://www.insidecounsel.com/2015/11/01/the-data-deficit
- 53. @DanLinna @LegalRnD Data-Driven World “Without data, you're just another person with an opinion.” ― W. Edwards Deming
- 54. @DanLinna @LegalRnD Daniel W. Linna Jr. Professor of Law in Residence Director of LegalRnD – The Center for Legal Services Innovation Blog: www.LegalTechLever.com LinkedIn: https://www.linkedin.com/in/danlinna/ Twitter: @DanLinna