Spring School on Argumentation in AI & Law Day 2 – lecture 2 Case-based Reasoning

Spring School on Argumentation in AI & LawDay 2 – lecture 2Case-based Reasoning Henry Prakken Guangzhou (China) 11 April 2018

Overview • Introduction to AI & Law • Case-based reasoning: HYPO, CATO & beyond

Part 1: Introduction to AI & Law

Some history on symbolic AI (& Law) •  1950 - 1970: modelling general intelligence • Newel & Simon’s General Problem Solver •  1970 - 1990: modelling expertise in limited domains • ‘Expert systems’, later ‘knowledge-based systems’ • Knowledge about a problem domain • Reasoning mechanisms for solving decision problems • E.g. MYCIN (diagnosis and treatment of infection diseases) • Since  1980: optimism about legal applications: • Model the rules in logic, reason logically

“Vehicles are not allowed in the park” • Facts: evidence problems • Legal conditions: general terms • Legal rules: exceptions • Purpose of the rule • Principle “Vehicles are objects meant for normal transport”

Legal reasoning is adversarial • Legal reasoning forms leave room for doubt • Legal cases involve clashes of interest Dispute • study constructingandattacking arguments

Knowledge-based AI for decision support • Knowledge-based systems: • Knowledge about a problem domain • Reasoning mechanisms for solving decision problems • Legal decision problems • Deciding court cases • Or arguing for decisions • Processing legislation in public administration • Giving legal advice • On litigation • On documents • On legal constructions • … • Regulatory compliance • …

Legal knowledge-based systems in practice • Quite a few rule-based systems in public administration • Don’t automate legal reasoning, but automate the logic of regulations • Proof of facts, classification and interpretation left to user • Until recently almost non-existent in court or advocacy • But recent developments, e.g. NeotaLogic

Limitations of legal rule-based systems • No handling of exceptions and rule conflicts • No cases • No argumentation

AI & Law research on legal argument • Legal reasoning as argumentation • Inference by constructing and comparing arguments and counterarguments • Applied to: • Determining the facts of a case (see tomorrow) • Legally classifying facts (today) • Applying legal rules to facts (see yesterday)

AI & Law models of legal argument • Systems for rule-based reasoning with rule exceptions and conflicts ... (Prakken & Sartor, Hage & Verheij, ...) • Systems for case-based reasoning: HYPO, CATO, … • Combining rule and cases: CABARET, … • Follow-up work (mostly formal): Bench-Capon, Gordon, Horty, Prakken & Sartor, Verheij, ... • But not yet applied in practice • Commonsense, empathy, fairness, … • Two overview papers: • H. Prakken, Legal reasoning: computational models. In J.D. Wright (ed.): International Encyclopedia of the Social and Behavioural Sciences, 2nd edition. Elsevier Ltd, Oxford, 2015. • H. Prakken & G. Sartor, Law and logic: a review from an argumentation perspective. Artificial Intelligence 227 (2015): 214-245.

New developments • Deep learning, natural-language processing, ubiquitous computing, big data • ML-techniques can now be applied at a large scale to unstructured data • IBM’s Watson: ‘knowledge-based’ system with access to huge amounts of unstructured information • Applied in medicine, financial trading, … • Now also in the law: ROSS (Un. Toronto) • http://www.rossintelligence.com/

IBM’s Watson static.guim.co.uk

‘Data centric’ models (1) • Require no model of legal reasoning or decision making • But require `task models’ of what lawyers do in their daily work L.K. Branting, Data-centric and logic-based models for automated legal problem solving. Artificial Intelligence and Law, 25(1):5–27, 2017.

‘Data centric’ models (2) • At case level: • Predicting probability of success • lexmachina.com • lexpredict.com • premonition.com • Partly based on factors unrelated to the merit of a case (court, judge, jurisdiction, parties, attorneys, …) • ... L.K. Branting, Data-centric and logic-based models for automated legal problem solving. Artificial Intelligence and Law, 25(1):5–27, 2017.

‘Data centric’ models (3) • At document level: • Information extraction • Persons, organisations, claims, outcomes … • Automated summarisation • legalrobot.com • casetext.com • Parsing statutory tekst • … L.K. Branting, Data-centric and logic-based models for automated legal problem solving. Artificial Intelligence and Law, 25(1):5–27, 2017.

‘Data centric’ models (4) • At corpus level: • Network analysis • Case citations, regulations, … • Judicial database analysis • Improving court management • Error detection • … L.K. Branting, Data-centric and logic-based models for automated legal problem solving. Artificial Intelligence and Law, 25(1):5–27, 2017.

Hybrid models • Argument mining • Mining (elements of) arguments • Guided by a model of argumentation • … • Watson’s debater function • Can this provide the input of advanced legal knowledge-based systems? K.D. Ashley, Artificial Intelligence and Legal Analytics. New Tools for Law Practice in the Digital Age. Cambridge University Press 2017.

State of the art • Simple rule-based systems: • in routine use in public administration • No model of legal argument • Advanced knowledge-based systems for legal argument: • much theory and proof of concept • Not scalable • Data-centric applications • Many practical applications • Not suitable for legal argument / decision making • Hope for the future: hybrid approaches

Part 2: Modelling legal classification and interpretation in factor-based domains

Factor-based reasoning • In legal classification and interpretation there are often no clear rules (not even clear defeasible rules) • Often there only are factors: tentative reasons pro or con a conclusion • To draw a conclusion, the sets of all applicable factors pro and con should be compared • This is done in cases, which become precedents • New cases should be decided according to procedent (stare decisis) • Problem: new cases are often not identical to a precedent

Running example factors: misuse of trade secrets • Some factors pro misuse of trade secrets: • F2 Bribe-Employee • F4 Agreed-Not-To-Disclose • F6 Security-Measures • F15 Unique-Product • F18 Identical-Products • F21 Knew-Info-Confidential • Some factors con misuse of trade secrets: • F1 Disclosure-In-Negotiations • F16 Info-Reverse-Engineerable • F23 Waiver-of-Confidentiality • F25 Info-Reverse-Engineered HYPO Ashley & Rissland 1985-1990 CATO Aleven & Ashley 1991-1997

Statistical techniques, machine learning … • Only useful if: • The relevant factors are known • Many precedents are available • The precedents are largely consistent with each other • Useful for predicting outcomes • But no model of legal reasoning • Black box • No argumentation

HYPO Ashley & Rissland 1987-1990 Kevin Ashley & Edwina Rissland • Representation language: • Cases: decision (p or d) + p-factors and d-factors • Current Fact Situation: factors • Arguments: • Citing (for its decision) a case on its similarities with CFS • Distinguishing a case on its differences with CFS • Taking into account which side is favoured by a factor

HYPO Example C1 (p) C2 (d) CFS • p1 p2 p3 • d1 d2 • p1 p2 p3 • d1 d2 • p1 p4 • d2 p2 p3 p4 d2 d3

HYPO Example C1 (p) C2 (d) CFS • p1 p2 p3 • d1 d2 • p1p2 p3 • d1 d2 • p1 p4 • d2 Distinguish! p2 p3 p4 d2d3 Distinguish!

HYPO Example C1 (p) C2 (d) CFS • p1 p2 p3 • d1 d2 • p1 p2 p3 • d1 d2 • p1 p4 • d2 p2 p3 p4 d2 d3

HYPO Example C1 (p) C2 (d) CFS • p1 p2 p3 • d1 d2 • p1 p2 p3 • d1 d2 • p1 p4 • d2 p2 p3 p4 d2 d3 Distinguish!

HYPO’s argument game • Given: a case base and a current fact situation • Plaintiff starts with citation • A case decided for plaintiff and sharing pro-plaintiff factors with the CFS • Defendant: • cites all counterexamples (cases citable for defendant) • distinguishes citation in all possible ways • On pro-plaintiff factors of precedent lacking in CFS • On new pro-defendant factors in the CFS • Plaintiff distinguishes defendant’s counterexamples in all possible ways

Citing precedent • Mason v Jack Daniels Distillery (Mason) – undecided. • F1 Disclosure-In-Negotiations (d) • F6 Security-Measures (p) • F15 Unique-Product (p) • F16 Info-Reverse-Engineerable (d) • F21 Knew-Info-Confidential (p) • Bryce and Associates v Gladstone (Bryce) – plaintiff • F1 Disclosure-In-Negotiations (d) • F4 Agreed-Not-To-Disclose (p) • F6 Security-Measures (p) • F18 Identical-Products (p) • F21 Knew-Info-Confidential (p)

Citing precedent • Mason v Jack Daniels Distillery (Mason) – undecided. • F1 Disclosure-In-Negotiations (d) • F6 Security-Measures (p) • F15 Unique-Product (p) • F16 Info-Reverse-Engineerable (d) • F21 Knew-Info-Confidential (p) • Bryce and Associates v Gladstone (Bryce) – plaintiff • F1 Disclosure-In-Negotiations (d) • F4 Agreed-Not-To-Disclose (p) • F6 Security-Measures (p) • F18 Identical-Products (p) • F21 Knew-Info-Confidential (p) Plaintiff cites Bryce because of F6,F21

Distinguishing precedent • Mason v Jack Daniels Distillery (Mason) – undecided. • F1 Disclosure-In-Negotiations (d) • F6 Security-Measures (p) • F15 Unique-Product (p) • F16 Info-Reverse-Engineerable (d) • F21 Knew-Info-Confidential (p) • Bryce and Associates v Gladstone (Bryce) – plaintiff • F1 Disclosure-In-Negotiations (d) • F4 Agreed-Not-To-Disclose (p) • F6 Security-Measures (p) • F18 Identical-Products (p) • F21 Knew-Info-Confidential (p) Plaintiff cites Bryce because of F6,F21 Defendant distinguishes Bryce because of F4,F18 and F16

Counterexample • Mason v Jack Daniels Distillery – undecided. • F1 Disclosure-In-Negotiations (d) • F6 Security-Measures (p) • F15 Unique-Product (p) • F16 Info-Reverse-Engineerable (d) • F21 Knew-Info-Confidential (p) • Robinson v State of New Jersey – defendant. • F1 Disclosure-In-Negotiations (d) • F10 Secrets-Disclosed-Outsiders (d) • F18 Identical-Products (p) • F19 No-Security Measures (d) • F26 Deception (p) Defendant cites Robinson because of F1

Distinguishing counterexample • Mason v Jack Daniels Distillery – undecided. • F1 Disclosure-In-Negotiations (d) • F6 Security-Measures (p) • F15 Unique-Product (p) • F16 Info-Reverse-Engineerable (d) • F21 Knew-Info-Confidential (p) • Robinson v State of New Jersey – defendant. • F1 Disclosure-In-Negotiations (d) • F10 Secrets-Disclosed-Outsiders (d) • F18 Identical-Products (p) • F19 No-Security Measures (d) • F26 Deception (p) Defendant cites Robinson because of F1 Plaintiff distinguishes Robinson because of F6,F15,F21 and F10,F19

K.D. Ashley. Modeling Legal Argument: Reasoning with Cases and Hypotheticals. MIT Press, Cambridge, MA, 1990. Plaintiff: I should win because My case shares pro factors F6 and F21 with Bryce, which was won by plaintiff Defendant: Unlike the present case, Bryce had pro factors F4 and F18 Defendant: UnlikeBryce, the present case has con factor F16 Defendant: I should win becausemy case shares con factor F1 with Robinson, which was won bydefendant Plaintiff: Unlike the present case, Robinson had con factors F10 and F19 Plaintiff: Unlike Robinson, the present case has pro factors F6, F15 and F21

Adam Wyner Trevor Bench-Capon & Katie Atkinson A logical account of case-based reasoning in factor-based domains H. Prakken, A. Wyner, T. Bench-Capon & K. Atkinson, A formalisation of argumentation schemes for legal case-based reasoning in ASPIC+. Journal of Logic and Computation 25 (2015): 1141-1166.

Basic scheme for reasoning with two-valued factors AS1: ThePro-factors of current are P TheCon-factors of current are C P arepreferred over C Current should be decided Pro ThePro-factors of current are P TheCon-factors of current are C C arepreferred over P Current should be decided Con

Preferences from precedents (1) AS2: ThePro-factors of precedent are P TheCon-factors of precedent are C precedent was decided Pro P arepreferred over C Limitation 1: the current case will often not exactly match a precedent

A fortiori reasoning with two-valued factors AS3: P arepreferred over C P+arepreferred over C- Limitation 2: not all differences with a precedent will make a current case stronger P+ = P plus zero or more additional pro-factors C- = C minus zero or more con factors

Vincent Aleven 1991-1997 (snapshot of)CATO Factor Hierarchy Misuse of Trade Secret (p) F120: Info legitimately obtained elsewhere (d) F101: Info Trade Secret (p) F104: Info valuable (p) F102: Efforts to maintain secrecy (p) F4: Agreed not to disclose (p) F1: Disclosures in negotiations (d) F6: Security measures (p) F15: Unique product (p)

V. Aleven. Using background knowledge in case-based legal reasoning: a computational model and an intelligent learning environment. ArtificialIntelligence 150:183-237, 2003. Distinguishing Misuse of Trade Secret (p) F120: Info legitimately obtained elsewhere (d) F101: Info Trade Secret (p) F104: Info valuable (p) F102: Efforts to maintain secrecy (p) F4: Agreed not to disclose (p) F1: Disclosures in negotiations (d) F6: Security measures (p) F15: Unique product (p)

Emphasising distinctions Misuse of Trade Secret (p) F120: Info legitimately obtained elsewhere (d) F101: Info Trade Secret (p) F104: Info valuable (p) F102: Efforts to maintain secrecy (p) F4: Agreed not to disclose (p) F1: Disclosures in negotiations (d) F6: Security measures (p) F15: Unique product (p)

Downplaying distinctions Misuse of Trade Secret (p) F120: Info legitimately obtained elsewhere (d) F101: Info Trade Secret (p) F104: Info valuable (p) F102: Efforts to maintain secrecy (p) F4: Agreed not to disclose (p) F1: Disclosures in negotiations (d) F6: Security measures (p) F15: Unique product (p)

Exploiting factor hierarchies (1):current misses pro factor AS4: P1 arepreferred over C P2 substitutesP1 P2 arepreferred over C Def1: Factor set P2 substitutesfactor set P1 iff For all factors p1 in P1 that are not in P2 there exists a factor p2 in P2 that substitutesp1 Def2: Factor p2 substitutes factor p1 iff p1 instantiates abstract factor p3 and p2 instantiates abstract factor p3

Current should be decided Pro ThePro-factors of Current are {F6,F21} {F6,F21} > {F1} TheCon-factors of Current are {F1} {F4,F21} > {F1} {F6,F21} substitutes {F4,F21} ThePro-factors of Precedent are {F4,F21} Precedent was decided Pro TheCon-factors of Precedent are {F1} F6 substitutes F4 F4 instantiates F102 F6 instantiates F102

From two-valued to many-valued factors (dimensions) • Dimensions can have a value from an ordered range of values • Numbers • Anything else that can be ordered • Notation: (dimension,value) or (d,v) • Dimensions have polarities: < con pro 0,1,2,…. .…, 500, ….... Primary school, secondary school, Bsc, Msc, Dr

Example dimensions in HYPO • Number of disclosees (0,1,….) • Competetive advantage (none, weak, moderate, strong) < pro con 0 1 2 3 4 5, …....

Example dimensions in HYPO • Number of disclosees (0,1,….) • Competetive advantage (none, weak, moderate, strong) < con pro none weak moderate strong

A fortiori reasoning with dimensions AS6: P1 arepreferred over C1 P2 are at least as strong as P1 C1 are at least as strong as C2 P2 arepreferred over C2 • Def5: • Set P2 of dimension-value pairs pro is at least as strong as set P1 of dimension-value pairs proiff • For all pairs (d,v1)in P1 there exists a pair (d,v2)in P2 such that v1 ≤ v2 • Set C1 of dimension-value pairs con is at least as strong as set C2 of dimension-value pairs con iff • For all pairs (d,v2)in C2 there exists a pair (d,v1)in C1 such that v1 ≤ v2

Example with dimensions (1) • Precedent – defendant • F1 Disclosure-In-Negotiations (d) • F21 Knew-Info-Confidential (p) • Fx Competetive-advantage = strong (p) • Fy Number of disclosees = 10 (d) • New case – undecided • F1 Disclosure-In-Negotiations (d) • F21 Knew-Info-Confidential (p) • Fx’ Competetive-advantage = moderate (p) • Fy’ Number of disclosees = 6 (d) {F21, Fx} < {F1,Fy} because of precedent Defendant wants to argue that {F21, Fx’} < {F1,Fy’}

Spring School on Argumentation in AI & Law Day 2 – lecture 2 Case-based Reasoning