Ǥȗ ȋDzdzȌ ǤȀ Ǧ Ȅ Ȅ Ǥ Ǧ Ǥǡ ǡ Ǧ ǡ ǡ Ǥ ǡ ǡ ǣ x x x x Ǣ Ǣ Ǧ Ǣ Ǥ Ǥǡǡ ȋǡ ǡ ȌǤǦ ǡ ǡ Ǥ ǡ Ǧ ǡshould ǡ Ǥǡ Ǧ ǯ ȗǡ Ǣ Ǧ ǡ ͺ͵ ͺͶ CHICAGOǦKENTLAWREVIEW ȏͺͺǣ͵ Ǣ Ǣǯ Ǣ ȋǡ ǡ ǡ ȌǢ Ǥ Ǧ ǡ Ǥ ǡ Dz dzǢ Ǥ Ǥ ǡ ǡ Ǥ ǯ ǡ ǡ ǡ Ǥ Ǧ ǡǡ Ǧ Ȅ Ȅ Ǥ ǡ Ǥ Ǧ Ǧ Ǥ ǡ Ǧ ǯ ǯ Ǧ Ǥ ǡ Ǧ ǡͳǡ Ǧ Ǧ ǡ ǡetceteraǡ ͳǤ ǯ ǤEǦ discovery ǡ ǡ Ǥ Ǧ Ǧ Ǥ ʹͲͳ͵Ȑ DIGITALAGELEGALPRACTICE ͺͷ ǡ ǡ Ǧ Ǥ Ǥ Ǣ ǯ ǨǦ Ǧ Ǥ ǯ Ǧ ǤǦ ǡ ǡ Ǥ ǤǡǦ ǡ Ǥ Ǧ ǡ ǡǦ ǡ ǡ Ǥ Ǧ ǤǦ ǡǦǦǦ Ǥ ǡ ǡ ǡ Ǧ ǡǡ Ǥ Ǥ ǡ Ǧ Ǥʹ Ǧ Ǥ Ǧ ǡ ʹǤ ȋǡǤǡͳͻͺȌǤ ͺ CHICAGOǦKENTLAWREVIEW ȏͺͺǣ͵ Ǥ ǡ Ǥ Ǧ ǤǡǦ Ǣ Ǥ Ǥ ǡ ǡ ǡ ǤǦ ȋ Ȍ Ǥ ǯ Ǧ ȀȋȀȌǡ ǣ ͳǤ ǣǡ ǡ ǫ ʹǤ ǣ Ǧ ǫ ͵Ǥ ǣ ǡ ǫ͵ ǡ Ǥ ǡ ǡ ǡ Ǧ Ͷ Ǧ ǡ ǯ Ǥͷ ǡ ǯ ǡ ǡ Ǥ ǡ Ǧ ͵Ǥ ǡǤǡǤǡǤǡǤ ǡ Ǥ ǡ Ǥ Ƭ Ǥ ǡ ǣ ͳͶͲǦͶʹǡͳǡͳͻʹȋʹǤͳͻͻͷȌǤ ͶǤ Ǥ Ƭ Ǥ ǡ ͳͶ ȋͳͻͺͳȌ ȋDzǮȏȐȏȐ ȏȐǤdzȌǤ ͷǤ ǤǡReasoningwithCasesandHypotheticalsinHYPOǡ͵ͶǯǤǦǤ Ǥͷ͵ǡ͵ǦͶȋͳͻͻͳȌǤ ʹͲͳ͵Ȑ DIGITALAGELEGALPRACTICE ͺ ǡ ǡ Ǥ ǡ ǡ ǯ Ǧ Ǧ ȋ ǡ ǡ ǡ ǡ ȌǤǡ Ǧ ǡ Ǥ ǡ ǡ ǡ Ǥ ǯ ǡ Ǧ ǡ Ǥ ǡ Ǥ ǯ Ȁ ǡ Ǧ Ǥ ǯ ǡ ǡǡ ǤǦ ǯȋȌ ǡǦ ǤȀǡ ǡ ǡǦ Ǥ Ǥ Ǥ ǡ ǡ Ǥ ǡ Ȅ Ȅdzdz ad hoc ǡ ǡ ǡ Ǧ Ǥ ǡͲȋʹǤʹͲͲͶȌǤ ͺͺ CHICAGOǦKENTLAWREVIEW ȏͺͺǣ͵ Ǥ ǡ Ǧ ǡ ǡ Ǥ Ǧ ǡ Ǥǯ Ǥ ǡ Ǧ Ǥ Ǥ Ǧ Ǥ ǡ Ȁ Ǥ Ǧ Ǣ ǤǦ ǡ ȋ Ȍ ǯȋȌ ǡ Ǧ Ǥ ǡ ǯȋǡ Ȍ ǡ Ǥ ǡ Ǥ ǡ Ǧ Ǧ Ǥ ǡ ǡ ǡ ǡͺ ǡ Ǥ ǤǤǡ ͳǦʹȋͳͻͻȌǤ ͺǤ Id.ͳȋDz ǤǤǤ ǤǤǤdataminingǦ ʹͲͳ͵Ȑ DIGITALAGELEGALPRACTICE ͺͻ ȋ Ǧ Ȍǡ Ǧ Ǥ Ǧ ǡ Ǥ ǡ ǡ ǡ ǡ ǡǡ ȋ Ȍ Ǥ ǡ ǡ Ǧ ǡ ǡ ǡ Ǥ ȋͳȌǦ Ǣ ȋʹȌ Ǣ ȋ͵Ȍ Ǧ Ǥ Ǧǡ ǡ ǡ ǡǡ ǡ Ǧǡ Ǥͻ ǡ ǯ ǡ ȋȌǡͳͲ ȋȌǡͳͳArtifiǦ cial Intelligence and LawǤͳʹ Ȁ Ǥ Ȁ ǡ Ǧ ǡǡ Ǥ ȋǤǤǡ ȌǤǤǤȏǤȐdzȌȋȌǤ ͻǤ See infra ͳ͵ǡ ͳǡ ʹ͵ǡ ʹͶǤ ͳͻͺͻǤ ͳͲǤ ǡ ǣȀȀǤǤ ȋǤʹ͵ǡʹͲͳ͵ȌǤ ͳͳǤ ǡ ǡ ǣȀȀǤǤ ȋ ǤʹͲǡʹͲͳ͵ȌǤ ͳʹǤ See ƬǤǡavailableat ǤǤ Ȁ ȀȀͳͲͷͲǤ ͻͲ CHICAGOǦKENTLAWREVIEW ȏͺͺǣ͵ ǡ Ǥ Ǧ ȋ Ǧ ȌǤ Ǧ Ǧ ǡ ǡ Ǥ Ǧ ǡ ǡ ǡ Ǥ ǡ ǡ Ǧ ǡͳ͵ǡ Ǧ Ǥ Dzdz Ǥ Ǧ ǡǡ Ǥ ǡǤ ǡ Ǧ Ǧ ǡǦ Ǧ Ǥ Ǧ Dzdz ȋ ȌǦǤ Ǥ ǡǡǡ Ǥ Ǧ ǡ Ǧ Ǧ ͳ͵Ǥ Ǧǡ Dz dz ǣ ǡ ǡ ǡǦ ǡ ǡ Ǥ Dz dz Ǧ ǡ ǡ Ǧ ǡǡǤ ʹͲͳ͵Ȑ DIGITALAGELEGALPRACTICE ͻͳ Ǥ ǡ Dz ǡǡ ǡdzǦ Ǧ ǤͳͶ Ǧ ȋ Ǧ ͳͷȌ Ǧ Ǥ ǡ ǯǡ ǡ Ǥͳ Ǥ Ǧ Ȅ Ǧ Ȅ Ǥ A.PartOne:ComputationalModelsofReasoningwithLegalRulesand Cases ǡIntroductiontoComǦ putational Models of Legal Reasoningǡ Ǧ ǡǦ Ǥ ǡ ǡ ǡǯ Ǧ ǡ Ǧ Ǥͳ ǯ ͳͶǤ ǡ ǡ Ǥǡ Ǧ ȋǤ ʹͶǡ ʹͲͳʹȌ available atǡ ǣȀȀǤǤ ȀȀ Ȁ ȀͻͶ͵ͺͻͲǤǤ ͳͷǤ SeeinfraͶͲǤ ͳǤ ǡ Ƭ ǡ ǣȀȀǤǤ ȋ Ǥ ʹ͵ǡʹͲͳ͵ȌǤ ͳǤ Ǥ ǡ Artificial Intelligence and Law: Stepping Stones to a Model of Legal Reasoningǡͻͻ Ǥ ǤͳͻͷǡͳͻͷǦͷͺȋͳͻͻͲȌǤǡ ǡ ǤǡȋǦ ͻʹ CHICAGOǦKENTLAWREVIEW ȏͺͺǣ͵ ȋǤǤǡ ǡ Ȍ Ǥͳͺ Ǥͳͻ Ǥǯ Ǧ Ǥ ǯ ǡǡ ǣǦ ǡ ǡ Ǣ Ǧ Ǣ ȋ ǡ ȌǤ Ǧ Ǣ ǡ ǡ Ǥ ǡ Ǧ ǡDzdzȋǡ Ȍ Ǥ ǯ ǣȋͲȌǡȋͳȌǡȋʹȌǦ ǡȋͳʹȌ Ǥ Ǥ ǡ ǣ ȋͲȌ ȋͳȌ Ǧ ǡȋͳȌȋʹȌ ǡ ȋʹȌ ȋͲȌ Ǧ Ǥ ǡ ǡ ȌǦ ǣͳȌȋǤǤǡǦ Ǧ Ȍ ǡ ʹȌ ǡ ͵Ȍ ǡͶȌ Ǧ ǡ ǡ ǡ Ǥ ǡǤ ͳͺǤ Ƭ ǡsupraͶǤǦ Ǥ ͳͻǤ ǡ Ͷ ȋͳͻͺȌǤ ǯ Ǥ ʹͲͳ͵Ȑ DIGITALAGELEGALPRACTICE ͻ͵ ȋǡ ǡ Ǧ ǡ ǡetceteraȌǤ LogicalModelsofStatutoryReasoning Ǧ ǡ et aliaǡ Ǧ Ǣ Dz Ǥdz ǡ ͳͷͲ ȋȌ ǡ Ǧ ǤʹͲ ǣ Ǥ Ǧ ǯ Ǥ Ǧ Ǧ Ǧ ǯ Ǥʹͳ Ǥ ǡ Ǧ Ǥʹʹ Ǣ ǡ Dz dz Ǥ ǡ ǡ ǡǡǤʹ͵ ʹͲǤ ǤǡǡǤǡ ǡǤƬǤǤ ǡ The British Nationality Act as a Logic Programǡ ʹͻ Ǥ ͵Ͳ ȋͳͻͺȌǤ ǡǤ ʹͳǤ ǦƬǡǣDevelopmentofaKBSto Support British Coal Insurance Claimsǡ ͳͻͻͳ Ǥ ǯ Ǥ ƬǤʹǤ ʹʹǤ SeeǤƬǤǡNormalizedLegalDraftingandtheQueryMethǦ odǡʹͻǤ Ǥ͵ͺͲȋͳͻǦͳͻͺȌǤǡǦ ǡ ǡ ǡǡ Ǯ ǡǡ Ǥ ʹ͵Ǥ Ƭ ǡ ObstaclestotheDevelopmentofLogicǦBased Modelsof Legal Reasoningǡ in ͳͺ͵ǦʹͳͶ ȋ Ǥǡ ͳͻͺȌǤ ǡ Ǧ Ǧ ǡ ǡǦ ǡǡǤ ͻͶ CHICAGOǦKENTLAWREVIEW ȏͺͺǣ͵ CaseǦBasedModelsofLegalReasoningModelsforPredictǦ ing Legal Outcomes Ǧǡ Ǥ Ǧ and Ǧ ǡʹͶ Ǥʹͷ ǡ ǡ ȋcriterialȌǤʹ ȋ ǯǦʹȌǡǦ Ǥ Ǧ Ǧ Ǧ ǡ ǤǤ Ǥ ȋ ǡ Ȍǡǯ ǯǤʹͺ Ǧ Ǧ ǡʹͻ ͵Ͳǡ Ǥ ǡ Ǧ Ǧ ʹͶǤ See Ǥ ǡ An Implementation of Ǥ ǡ ͳͻͻͷ Ǥ ǯ Ǥ Ƭ ǤʹǤ ǡ Ǥ ʹͷǤ ǡsupraͷǡ͵ǦͶǤ ʹǤ ǤǡBuildingExplanationsfromRulesandStructuredCasesǡ͵ͶǯǤǦ ǤǤͻȋͳͻͻͳȌǤ ʹǤ ǡsupraͷǡͶǤ ʹͺǤ ǡsupraʹǤ ʹͻǤ See ǡUsingBackgroundKnowledgeinCaseǦBasedLegalReasoning:AComǦ putational Model and an Intelligent Learning Environmentǡ ͳͷͲ ͳͺ͵ ȋʹͲͲ͵ȌǤ ͵ͲǤ See Ǥ Ƭ òǡ Computer Models for Legal Predictionǡ Ͷ ͵ͲͻȋʹͲͲȌǤ ʹͲͳ͵Ȑ DIGITALAGELEGALPRACTICE ͻͷ Ǥ͵ͳ ǡǡ Ǥ ModelsIntegratingCases,Statutes,Rules,ConǦ cepts,andValues Ǧ Ǧ Ǥ Ǧ Ǧ ǡ ǯ Ǧ Ǥ͵ʹ ǡǡ Ǧ Ǧ ǯ Ǧ Ǥ Ǥ͵͵ ǡ Ǧ Ǧ ǡ ǯǤ ͳͻͻ͵ǡ Ǧ Ǥ͵Ͷ Ǥ ǣ Ȅ Ȅ Ǥ ǡ Ǧ ǯ Ǥ͵ͷ Ǧ Ǥ Dzǯdzȋ ȌǤ͵ ǡ Ǥ ǯ Ǥ ͵ͳǤ See ƬǡPredictingJudicialDecisions:TheNearestNeighbour RuleandVisualRepresentationofCasePatternsǡ͵ ͵ͲʹȋͳͻͶȌǤ ͵ʹǤ ǡsupraʹǤ ͵͵Ǥ Ǥ Ƭ Ǥ ǡ CABARET: Statutory Interpretation in a Hybrid Architectureǡ͵ͶǯǤǦǤǤͺ͵ͻȋͳͻͻͳȌǤ ͵ͶǤ Ǥ Ƭ ǡ Representing Teleological Structure in CaseǦBased LegalReasoning:TheMissingLinkǡͳͻͻ͵ǤǯǤ ƬǤͷͲǤ ͵ͷǤ ǦƬ ǡAModelofLegalReasoningwithCasesIncorpoǦ ratingTheoriesandValuesǡͳͷͲ ͻȋʹͲͲ͵ȌǤ ͵Ǥ See Ǥ ǡExtensionallyDefiningPrinciplesandCasesinEthics:AnAIModelǡ ͳͷͲ ͳͶͷȋʹͲͲ͵ȌǤ ͻ CHICAGOǦKENTLAWREVIEW ȏͺͺǣ͵ B.PartTwo:DefeasibleLegalReasoningwithArgumentSchemes ǡ Ǧ ǡ Ǧ ǡ Ǧǡ Ǧ Ǥ Ǧ can ǡ Ǧ Ǥ AssessingLegalClaimswithArgumentSchemaǦ Ǧ Ǧ͵ Ǥ ͵ͺ Ǥ ǡ Ǧ ǡ ǤǤ Ǥ͵ͻǤǯ Ǧ ǤͶͲ Ǧ ǡ Ǥ ǡǤ ͶͳǡͶʹ Ǥ ǡͶ͵ ͵Ǥ Ƭ ǦǡArgumentationandStandardsofProofǡʹͲͲǤ ǯǤ ƬǤͳͲǤ ͵ͺǤ Ǥ Ƭ ǡ Legal Reasoning with Argumentation Schemesǡ ʹͲͲͻǤǯǤ ƬǤͳ͵Ǥ ͵ͻǤ Ƭ Ǥ ǡ Facilitating Case Comparison Using Value JudgǦ ments and Intermediate Legal Conceptsǡ ʹͲͳͳ Ǥ ǯ Ǥ Ƭ Ǥ ͳͳǢ ƬǤǡArgumentationwithValueJudgments ǦAnExampleofHypotheticalReasoningǡʹͲͳͲǤ ʹ͵ Ǥ Ǥ Ƭ ǤǤǤ ͶͲǤ Ǥ ǡ ǡ Ǥ Ƭ ǡ A Framework for the Extraction and Modeling of FactǦFinding Reasoning from Legal Decisions: Lessons from the VacǦ cine/InjuryProjectCorpusǡͳͻ ƬǤʹͻͳǡʹͻǦͻͺȋʹͲͳͳȌǤ ͶͳǤ See ǡʹͶǤ ͶʹǤ ǡsupraͳǢƬǡsupra͵͵ǡͺ͵ͻǦͶͲǤ Ͷ͵Ǥ Ǥ Ƭ òǡ A Predictive Role for Intermediate Legal ConǦ ceptsǡʹͲͲ͵ǤǤǤ ƬǤǤͳͷ͵ǡͳͷͷǤ ʹͲͳ͵Ȑ DIGITALAGELEGALPRACTICE ͻ Ǥ ǡͶͶ Ǧ Ǧ ǤͶͷ Ȅ Ǧ Ȅ Ǥ Ǥ RepresentingLegalConceptsandCaseKnowledgeinOnǦ tologiesǢͶ Ǧ ǡ ǡ ǢͶǦǦǡ Ǧ ǤͶͺ C.PartThree:LegalInformationRetrieval,InformationExtraction,and TextProcessing ǯ ExplainingHowFullǦTextLegalInformation Retrieval Works Ǧ ȋDzdzȌȋ Ȍǡ ǡ Ǧ ǡ ͶͻǡͷͲ ͶͶǤ ǤƬǤǡDetectingChangeinLegalConceptsǡͳͻͻͷǤ ǯǤ ƬǤͳʹǤ ͶͷǤ Ƭǡsupra͵ͻǡͳͶǦǤ ͶǤ ǤǤ Ǧ Ƭ ǤǤ ǡ Ontologies in Legal Information Systems; The Need for Explicit Specifications of Domain Conceptualisationsǡ ͳͻͻ Ǥ ǯ Ǥ Ƭ Ǥͳ͵ʹǢǡƬǡLegal OntologiesinKnowledgeEngineeringandInformationManagementǡͳʹ ƬǤ ʹͶͳȋʹͲͲͶȌǤ ͶǤ SeeǤǡOntologicalRequirementsforAnalogical,Teleological,andHypothetǦ icalLegalReasoningǡʹͲͲͻǤǯǤ ƬǤͳǤ ͶͺǤ ǡǡƬ ǡIntegrating a BottomǦUp and TopǦDown Methodology for Building Semantic Resources for the Multilingual LegalDomain,in ǣ ͻͷ ȋ ǡ ǡ Ƭ ǤǡʹͲͳͲȌǤ ͶͻǤ ǡ Text Retrieval in the Legal Worldǡ ͵ Ƭ Ǥ ͷǡ ͷǦ ȋͳͻͻͷȌǤ ͷͲǤ SeeǡBayesianNetworksWithoutTearsǡͳʹ ǡͳͻͻͳǡ ͷͲǤ ͻͺ CHICAGOǦKENTLAWREVIEW ȏͺͺǣ͵ ǡ ǡͷͳ Ǥͷʹ CombiningLegalIRandAItoAnalyzeLegalClaimsǦ Ǧ Ǥ ǡ Ǧ Ǧ ǡͷ͵ ǡͷͶ ǡͷͷ ǡ Ǥǡ Ǥͷ ǡ ǡǦ Ǧ Ǥͷ LegalInformationExtractionandText Processing Ǥ Ϊ Ǧ Ǥͷͺ ǡ ǡ ȋ Dz dz Dz dzȌ ȋ ǡǡȌ ȋ ǡ ǡ ǡ Ȍ ȋ ǡ functional Ǧ ͷͳǤ ǤƬǤǤǡAnEvaluationofRetrievalEffectivenessforaFullǦTextDocǦ umentǦRetrievalSystemǡʹͺǤʹͺͻȋͳͻͺͷȌǤ ͷʹǤ ǡǦǡƬ ǡInformationExtractionfrom CaseLawandRetrievalofPriorCasesǡͳͷͲ ʹ͵ͻȋʹͲͲ͵ȌǤ ͷ͵Ǥ ǤƬǤǡFindingLegallyRelevantPassagesinCaseOpinionsǡ ͳͻͻǤǯǤ ƬǤ͵ͻǤ ͷͶǤ ǤǡǤƬǤǡImprovingLegalInformationRetrievalUsingan OntologicalFrameworkǡͳ ƬǤͳͲͳǡͳͲ͵ȋʹͲͲͻȌǤ ͷͷǤ ǤǡsupraͷʹǤ ͷǤ Ǥ Ƭ Ǥ ǡ A Connectionist and Symbolic Hybrid for Improving LegalResearchǡ͵ͷǯǤǦǤǤͳȋͳͻͻͳȌǤ ͷǤ ǡƬǡBankXX:SupportingLegalArguments throughHeuristicRetrievalǡͶ ƬǤͳǡͷǦͺȋͳͻͻȌǤ ͷͺǤ ǤƬòǡAutomaticallyClassifyingCaseTextsandPredictǦ ingOutcomesǡͳ ƬǤͳʹͷǡͳ͵ͻǦͶͲȋʹͲͲͻȌǤ ʹͲͳ͵Ȑ DIGITALAGELEGALPRACTICE ͻͻ ȌǤͷͻ ǡ Ǥ ǡ Ǧ ǡ Ǥ AIandLawToolsforEǦ Discovery: Obtaining Evidence Relevant to Lawyers’ Hypotheses about Claims and Documents ǤͲ ǡ ǡͳ Ǧ Ǧ Ǥʹǡ Ǧ ǡ͵ ȋǡ Ǧ ͶȌǡ Ǥͷ D.PartFour:TheFutureofAIandLaw:BridgingComputationalModels andLegalTexts Ǥ Near Term Developments in AI and Law ͷͻǤ ƬǡAutomaticClassificationofProvisionsinLegislaǦ tiveTextsǡͳͷ Ƭ ǤͳǡʹǦ͵ȋʹͲͲȌǢǡƬ ǡMachineLearningVersusKnowledgeBasedClassificationofLegalTextsǡʹͲͳͲǤ ǦǤǤ ƬǤǤͺǤ ͲǤ SeeƬǡEmergingAI&LawApproachestoAutomatingAnalysis and Retrieval of Electronically Stored Information in Discovery Proceedingsǡ ͳͺ ƬǤ͵ͳͳȋʹͲͳͲȌǤ ͳǤ Ǥ ǡ Ǥ ǡ ǡ Ǥ Ƭ ǡ EvaluationofInformationRetrievalforEǦdiscoveryǡͳͺ ƬǤ͵ͶȋʹͲͳͲȌǤ ʹǤ ǤǡAfterword:Data,Knowledge,andEǦdiscoveryǡͳͺ ƬǤͶͺͳȋʹͲͳͲȌǤ ͵Ǥ ǡ NetworkǦbased Filtering for Large Email Collections in EǦDiscoveryǡ ͳͺ ƬǤͶͳ͵ȋʹͲͳͲȌǤ ͶǤ ǡǤƬǡAutomationofLegalSensemakingin EǦdiscoveryǡͳͺ ƬǤͶ͵ͳȋʹͲͳͲȌǤ ͷǤ ǡ ǯǡ ƬǦ ǡANewTangibleUser InterfaceforMachineLearningDocumentReviewǡͳͺ ƬǤͶͷͻȋʹͲͳͲȌǤ ͺͲͲ CHICAGOǦKENTLAWREVIEW ȏͺͺǣ͵ Ǥ ̻Ǧ Ǥ Ǧ ǡ Ǥ Ǧ ǡ ǡ ǡ ͺ ǯ ǦǤͻ ͲǦ Ǥͳ Ǧ Ǥ ǡ Ǥ Ǧ ǡ Ǧ Ǥ Ǥʹ Ǥ ǡ ǡ Ǧǡ ǡ ǡ Ǥ ǡ ǡ Ǥ ǡ ǡ ǡ Ƭ ǡBuildingWatson:AnOverviewoftheDeepQAProjectǡ͵ͳ ǡʹͲͳͲǡͷͻǤ Ǥ Ǥ Ƭ Ǥ ǡ Ǧ ǣ ȋ Ǧ ȌȋȌǤ ͺǤ ƬǤǡASurveyofUncertaintiesandtheirConsequences,in Probabilistic Legal Argumentation, in ͳǡ ͳǦʹ ȋ Ǥǡ ʹͲͳʹȌǤ ͻǤ ƬǡsupraͶǤ ͲǤ ƬǡRuleBasedBusinessProcessComplianceǡͺͶǤ ʹͲͳʹ̷ ǡǯǤǤͷȋʹͲͳʹȌǡavailable atǣȀȀ ǦǤȀǦͺͶȀͳǤǤ ͳǤ Ǥ ǡ Ƭ Ǥ ǡ A Declarative Approach to Business Rules in Contracts: Courteous Logic Programs in XMLǡ ͳͻͻͻ Ǥ Ǥ Ǥ ʹǤ ǡ ǣǦ ǢǦ ǡ ȋ ǤͷǡʹͲͳͲȌǡ availableat ǣȀȀǤ Ǥ Ȁ̴ Ȁ̴Ȁ̴̴̴ ǤǤ ǡǦ Ǥ ʹͲͳ͵Ȑ DIGITALAGELEGALPRACTICE ͺͲͳ E.Addenda:IntelligentTutoringSystemsandEthics ǡ Ǥ ǡ Intelligent Tutoring Systems in Law and Ethical Reasoning, Ǧ ǡ͵ ǡͶ Ǧ ǡͷ ǡ Ǥ ǡ Ǧ ȋ ȌǡǦ Ǥ ǡǡ Ǥ LegalandEthicalIssuesRelated toAIandLawProgramsǡ ǡͺ ǡͻ ǡͺͲ Ǧ Ǥͺͳ ǡ Ǧ Ǥ Ǥ ǡǦ Ǧ ǡ ͵Ǥ ǤǡICTinLegalEducationǡͳͲ ǤǤǤͻȋʹͲͲͻȌǤ ͶǤ SeeǡsupraʹͻǤ ͷǤ ǤǡUsingComputerSupportedArgumentVisualizationtoTeachLegalArgumenǦ tationǡin ǣ Ǧ ͷȋǤ ǡǤ ƬǤǤǡʹͲͲ͵ȌǤ Ǥ Ƭ ǡ Toward AIǦenhanced ComputerǦsupported Peer Review in LegalEducationǡʹͲͳͳǤǦǤǤ ƬǤǤͳǤ Ǥ ǡ Ǥ Ƭ Ǥ ǡ Comparing Argument DiaǦ gramsǡʹͲͳʹǤǦǤǤ ƬǤǤͺͳǤ ͺǤ Ƭ ǡ ͳͳͻ ȋʹͲͳͳȌǤ ͻǤ ǤǤǡIntelligentAgentsandLiability:IsItaDoctrinalProblemorMerelyA ProblemofExplanation?ǡͳͺ ƬǤͳͲ͵ȋʹͲͳͲȌǤ ͺͲǤ ǤǡTheUseofLegalSoftwarebyNonǦLawyersandthePerilsofUnauthorised Practice of Law Charges in the United States: A Review of Jayson Reynoso Decisionǡ ͳͺ ƬǤʹͺͷȋʹͲͳͲȌǤ ͺͳǤ ǡ Intellectual Property Rights in Virtual Environments: Considering the RightsofOwners,ProgrammersandVirtualAvatarsǡ͵ͻǤǤͶͻȋʹͲͲȌǤ ͺͲʹ CHICAGOǦKENTLAWREVIEW ȏͺͺǣ͵ Ǥ ǡ ǡ ǡ Ǥǡ ȋǡ Ȍǡ Ǧ Ǥǡ Ǥ Ǧ Ǧ Ǥ ǡ Ǥ Ǥ ǡ ͳ Ǥ A.Lessonsaboutlegalrules LessonA1:Legalrulesaresubjecttosemanticambiguity. LessonA2:Legalrulesaresubjecttologicalambiguity. LessonA3:Statutorystructureaffectsthemeaningoflegalrules. LessonA4:Legalrulesaresubjecttounstatedconditions. B.Lessonsaboutreasoningwithlegalcases LessonB1:Distinguishinghardfromeasycasesoflawisakeyprocess. LessonB2:AnalogizinganddistinguishingcasesarespecifiableproͲ cesses. LessonB3:ReasoningwithcasesandvaluesisakindoftheoryconͲ struction. LessonB4:Legaladvocatesposehypotheticalcasestotestlegalrules. C.Lessonsaboutlegalargument LessonC1:AdvocatesemploylegalͲdomainͲspecificargument schemes. LessonC2:OnemayattackalegalargumentbyattackingitsassumpͲ tionsoridentifyingexceptions. LessonC3:LegalargumentsinvolvecomplexrelationshipsamonglogͲ ic,rhetoric,uncertainty,andnarrative. ʹͲͳ͵Ȑ DIGITALAGELEGALPRACTICE ͺͲ͵ D.LessonsaboutlegalDigitalDocumentsTechnologies LessonD1:LegalDigitalDocumentsTechnologiesarebasedonprocess modelsandalgorithms. LessonD2:ProcessesunderlyinglegalDigitalDocumentsTechnologies involvedifferentkindsoftextsasinputs. LessonD3:DigitalDocumentsTechnologiesfindtextsrelevanttolegal problems. LessonD4:DigitalDocumentsTechnologiesandtheirprocessmodels needempiricalmethodologiesfortesting. LessonD5:Findingtextsrelevanttolegalproblemsisdifferentfrom applyingrelevanttextstosolvelegalproblems. LessonD6:Computationalmodelsoflegalreasoningcanbeabridge betweenlegaltextsandlegalproblemsolving. Figure 1: Lessons law students can learn from the materials in the AI and Law Seminar Syllabus Ǥ ǡǡ ǡ ǡ ǡ Ǥ ǤǦ DzǨdzȄ ǯǤ Ǥ A.LessonsAboutLegalRules LessonA1:LegalrulesaresubjecttosemanticambiguityǤǦ ǡ Ǥ ǡ Ǥ ͺʹ ǡ ǡ ǣ ǡ ǡ Ǥ ͺʹǤ SeeǤǡsupraʹͲǤ ͺͲͶ CHICAGOǦKENTLAWREVIEW ȏͺͺǣ͵ ǡ ǡ Ǧ ǦǤ ǡ ʹ Dz dz ǡ ǡDz dz Dz ǡdz Ǥͺ͵Ǧ ǡ ǡ DzdzDzǡdznotǦ Ǥ Ȅ Dz ǡdz Dz ǡdz Dzǡdz Dzǡdz Dz ǡdz Dz ǡdzȄǡǦǤǦ ǦǦ Ǥ [RULE4:STRICTLIABILITYDEFINITION] IF(theplaintiffisinjuredbytheproduct or(theplaintiffdoesrepresentthedecedent andthedecedentiskilledbytheproduct) ortheplaintiff’spropertyisdamagedbytheproduct) andtheincidentalͲsaledefenseisnotapplicable and(theproductismanufacturedbythedefendant ortheproductissoldbythedefendant ortheproductisleasedbythedefendant) andthedefendantisresponsiblefortheuseoftheproduct and(Californiaisthejurisdictionofthecase ortheuseroftheproductisthevictim orthepurchaseroftheproductisthevictim) andtheproductisdefectiveatthetimeofthesale and(theproductisunchangedfromthemanufacturetothesale or(thedefendant’sexpectationis‘theproductisunchanged fromthemanufacturetothesale’ andthedefendant’sexpectationisreasonableͲandͲproper)) THENassertthetheoryofstrictͲliabilitydoesapplytotheplaintiff’sloss Figure 2: Waterman’s Rule Defining Strict Liability Ǧ ǡ Ǥ Dz ǡdz ͺ͵Ǥ ƬǡͶǡͳǡ͵ͺǤ ʹͲͳ͵Ȑ DIGITALAGELEGALPRACTICE ͺͲͷ ǡ ǡǡ Ǥ Ǧ Ǧ ǣ ͳǤ ʹǤ ͵Ǥ ͶǤ ȋ Ǧ Ȍ Ǣ Ǣ Ǣ ǯ Ǧ ǯǤͺͶ Ǧ Ǥ ǡ ǦǤ Ǧ ǡ ǡ Ǧ Ǥ Ǥ ǡ Ǧ Ǧ Ǥ Ǥͺͷ Lesson A2: Legal rules are subject to logical ambiguity. Ǧ ǡ Ǥ logic Ǧ Ǥ ǡ Ǧ ǡ Ǧ Ǥ ǡ Ǥ ͺͶǤ Id.ʹǤ ͺͷǤ See,e.g.,ǡ supraʹǢ ǡsupra͵Ǣ Ƭ ǡ supra Ǥ ͺͲ CHICAGOǦKENTLAWREVIEW ȏͺͺǣ͵ Ǥ ǡǡDz ǤǤǤ ǡǡǡǡ Ǧ ǡ Ǥdzͺ ǡ ǡ Ǧ andǡ ǫ ǡ ǡ Ǥ ǡ Ǥ Ǧ ǯ ǡǤ Ȅ ǡ Ǧ Ȅ Ǧ Ǥ ǡ ǡ ǡ Ǥ ǡ Ǥ ǡ Ǧ Ǥ ǯ Ǧ Ǥͺ ǡ ǯǤ Ǧ Ǧ ȋ Ȍ ǯ Ǥ ǯ Ǧ Ǧ Ǧ Ǥ Ǧ ǡ Ǥ ͵ͷͶ ǡ ǡǤǤ ǡ Dz Ǥdzͺͺ ͺǤ Ƭǡsupraʹʹǡ͵ͺͶȋǤǡͳͷǤʹͶʹǡͶʹȋͳͻ͵ȌȌǤ ͺǤ Id.͵ͻȋDzȏȐ ǡ Ǥ ǤdzȌǤ ͺͺǤ Id.͵ͺͺǤ ʹͲͳ͵Ȑ DIGITALAGELEGALPRACTICE ͺͲ ǯ Ǥͺͻ ǯ Ǧ is Ǥ Ǥ LessonA3:StatutorystructureaffectsthemeaningoflegalrulesǤǦ ǯ ȋ ȌǡǦ Ǧ Ǥ Ǧ ǯ Ǥ Ǧ ǡ Ǥ Ǧ ǤͻͲ ǡ Ǧ Ǥ Ȅ ǡǯ Ǧ Ȅ Ǥ LessonA4:Legalrulesaresubjectto unstatedconditionsǤ ǡ ǡ ǡ ǡ ǡ Ǧ Ǥ Ǧ ǡ ǤǦ Ǥ Ǧ ǡ ͺͻǤ Id.͵ͻ͵ǡǤͶǤ ͻͲǤ But see Ǧ Ƭ ǡ supra ʹͳǢ ǡ Hierarchically Organised FormalisationsǡͳͻͺͻǤǯǤ ƬǤʹͶʹǤ ͺͲͺ CHICAGOǦKENTLAWREVIEW ȏͺͺǣ͵ ǯ Ǧ Ǥͻͳ Obstacles to the Development of LogicǦBased Models of Legal Reasoningǡǡ ǡ Ǧ ǡ Ǥ ǡ ǡ ǯ Ǧ ǡ Ǧ Ǥ ǡ ǡ Ǧ Ǥͻʹ Riggsv.Palmerͻ͵ Ǥ Riggs ǯǤ Dz dzǤ Dz ǡ Ǧ Ǥdz Ǧ Ǥ Ǥ ǡ ǡ Ǧ ǣ Ǧ Ǧ Ǥ ǡ ǡǦ ȋ Ȍ Ǥ ǡ ǡ ȋ ǡ ǡ ǡ Ȍǡ ǡ ǤͻͶ ͻͳǤ Ƭǡ SomeProblemsinDesigningExpertSystemstoAidLegal ReasoningǡͳͻͺǤǯǤ ƬǤͻͶǡͳͲͷǤǡ Dz Ǥdz Ǥ Dz Ǥdz Dz Ǥdz Ǥ Dz Ǥdz ͻʹǤ Ƭǡsupraʹ͵Ǥ ͻ͵Ǥ ʹʹǤǤͳͺͺȋǤǤͳͺͺͻȌǤ ͻͶǤ Ƭǡsupraʹ͵ǡͳͻͳǤ ʹͲͳ͵Ȑ DIGITALAGELEGALPRACTICE ͺͲͻ ǡ Ǥ ǯ ǡ ǡ ǡ Ǥ ǡ ǡ ǡ aboutwith Ǥ Ǧ ǡ ǡ Ǥ Ǧ ǡ ǡ Ǥ ǡ ȋ ǡǡ Ȍ ǡ Ǥͻͷ B. LessonsAboutReasoningwithLegalCases LessonB1:DistinguishinghardfromeasycasesoflawisakeyproǦ cess. Ǥ ǡ ǡ Ǧ ǡǦ ǫ Ǣ Ǩ Ǧ ǡ ǤǤǤǡͻ ǡ ǡ Ǥǡ ǡ ǡ Ǧ ͻͷǤ ǡ Ǥ ͻǤ See,e.g.,Ƭǡsupraʹ͵ǡͳͺͷǦͺǤ ͺͳͲ CHICAGOǦKENTLAWREVIEW ȏͺͺǣ͵ ǯ Ǧ Ǥǡǯ ǡ Ǧ Ǥ ǡ Ǥ ǦǦ ǡ ǤȋȌǦ Ǥ Ǧ Ǥ Ǥ ǡǤ Ǧ ǡ Ǥ ǡ ǡ Ǥ ͵ Ǥͻ Foreverypredicateintherule, Ifcommonsenseknowledgerulesprovideananswer,thenCSKͲAnswerisTrue,elseFalse. Ifproblemmatchespositiveexamplesofpredicate,thenPosͲExamplesisTrue,elseFalse. Ifproblemmatchesnegativeexamplesofpredicate,thenNegͲExamplesisTrue,elseFalse. If¬CSKͲAnswer If¬(PosͲExamplesorNegͲExamples)ͲͲ>QuestionisHARD If(onlyoneofPosͲExamples/NegͲExamples)ͲͲ>QuestionisEASY If(PosͲExamplesandNegͲExamples)ͲͲ>QuestionisHARD IfCSKͲAnswer If¬(PosͲExamplesorNegͲExamples)ͲͲ>QuestionisEASY If(onlyoneofPosͲExamples/NegͲExamples) If(agreesonlyͲonew/CSKͲAnswer)ͲͲ>Questioneasy,elseQuestionisHARD If(PosͲExamplesandNegͲExamples)ͲͲ>QuestionisHARD Figure 3: Gardner's Heuristic Method for Distinguishing Hard from Easy Cases ǯ ͻǤ ǡsupraͳǡͳͻͲǡǤʹǢ ǡsupraͳͻǡͷͶǦͷͷǡͳͲǦͳǤ ʹͲͳ͵Ȑ DIGITALAGELEGALPRACTICE ͺͳͳ ǡǡǡȋȌ Ǥ ǯ Ǧ ǤǡǦ Ǥ ǯǡǦ ǡ ǡ ǡ DzǤdzǡ Ǧ Ǥ Ǥ Ǧ ǯ Ǧ Ǧ ǡ ǯ Ǥ Ǥ ǡ Ǥ LessonB2:AnalogizinganddistinguishingcasesarespecifiableproǦ cessesǤ Ǧ Ǥǡ ǡDzǦ dzDz dzǡ Ǥ ǯ ǡ ǡ Ǥ Ǥ Ǧ Ǥ ǡ Ǧ Ǥ Ǥ ͳǡǦ Ǥ Ǧ ǡǣ x ǡ ͺͳʹ CHICAGOǦKENTLAWREVIEW x x ȏͺͺǣ͵ ǡ ǡ ǡ ǯ ǡǡ ǯǦ Ǥ ǡ ǡ Ǥ Ǥ ǡ ǡ ǡ Ǥ Ǥ ʹͲͳ͵Ȑ ͺͳ͵ DIGITALAGELEGALPRACTICE Table 1: Computational Measures of Relevance Relevance Representation Definition Domain/Example RelevanceMeasǦ ure Source ȋͶ ǡ Ȍ Ȁ ǣǯ ȋͳ͵ Ǧ Ȍ Ȁ ǣǯ ȋͳͶ ǡ Ȍ Ǧ ȋʹ Ȍ Ǧ ȋͳ͵ Ȍ Ǧ Ȁ ǡ Ȁ ǦǦ Ǧ Ƭ ǡsupra ͵ͳǤ ǡsupra ͷǤ Ǧ Ȁ Ǧ Ǧ ǣǦǦǦ Ƭ ǡsupra ͵͵Ǥ Ȁ ǦǦȋ Ȍ Ȁ ǡsupra ʹͻǤ ȀǦǦ ȀǦ Ǧ ȋǦ Ȍ Ǧ ȋͶ ͵ Ȍ ȋȌ ǡ Ǧ Ǧ Ǥ ȀǦ ǦǦǦ Ǥ ƬüǦ ǡComǦ puterModelsǡ supra͵ͲǤ ǡsupra ʹǤ Ǧ ȋȌ ǯ Ǧ Ǥ ȋǤǤǡ Ȍ ǡsupra ʹͻǤ ǦƬ ǡsupra ͵ͷǤ ǡsupra ͵Ǥ ͺͳͶ CHICAGOǦKENTLAWREVIEW ȏͺͺǣ͵ ͳ Ǥ ǡ ǡǦ Ǥ notǡ Ǥǡ ǢǤǦ ǡ ǡ Ǧ Ǥ Ǥ ǡ ǯ ǡ Ǥ ǡ Ǧ ǡǡ Ǥ ǡ ǡǡͻͺǦ Ǧ Ǥ ǡ ǡ Ǥ ǡ Ǥ Ǧ Ǣ Ǧ Ǥǡ ǯ Ǥ ǡ ǡ Ǧ Ǥ LessonB3:ReasoningwithcasesandvaluesisakindoftheoryconǦ struction. ǡ Ǧ Dz Ǥdzͻͻ ǡ ͻͺǤ Ƭǡsupra͵ͶǤ ͻͻǤ ǦƬǡsupra͵ͷǡͻͺǤ ǡ supraʹͶǡʹͺͷǣDzǮ ǯ ʹͲͳ͵Ȑ DIGITALAGELEGALPRACTICE ͺͳͷ ǡǡǡ Ǧ Ǥ Ǥ Ǧ ǡ Ǥ Ǥ ǡǤǡ Ǣ ȋ ǡ Ȍǡ ǡ Ǥ ǡ Ǥ ǡǡ ǤǦ Ǥ ǡ ǯ Ǥ ǡ Ǥ ǡ Ǥ ǡ ǡ Ǧ ǡ ǡ Ǧ Ǥ ǡ ǡdo ǫ Lesson B4: Legal advocates pose hypothetical cases to test legal rulesǤ Ǧ Ǥ Ǧ Ǥ ǡ ǡ Ǧ Ǥ ǡ Ǧ Ǥ ǡ ǡ ǡ Ǥdz ͺͳ CHICAGOǦKENTLAWREVIEW ȏͺͺǣ͵ Ǥ ǡ ǡ Ǧ ǣ ǡ ǡ ǫ ǤͳͲͲ ǡ Ǧ Ǥ ǯ Ǧ Ǥ ǡ Ǧ Ǥ ǡ ǣ Ǥ Ǥ Ǧ ǡ Ǧ ǤͳͲͳ ǡ ǡ Ǧ Ǥ ǣ x x x x ǡ Ǧ ǡ ǡ ǡǦ ǡ Ǥ ͳͲͲǤ See,e.g.,ǡsupra ͷǢǤǡ TeachingaProcessModel ofLegalArguǦ ment with Hypotheticalsǡ ͳ Ƭ Ǥ ͵ʹͳ ȋʹͲͲͻȌǢ Ƭ ǡ supra ͵ͻǤ ͳͲͳǤ ǡTeaching aProcess Modelǡ supra ͳͲͲǡ͵ʹǦʹǢ Ƭ ǡ supra ͵ͻǡͳͶǤ ʹͲͳ͵Ȑ DIGITALAGELEGALPRACTICE ͺͳ Ǥ Ǧ ǡ Ǧ Ǥ C. LessonsAboutLegalArgument Lesson C1: Advocates employ legal domainǦspecific argument schemes.ǡ Ǧ ǡ ǡ ǡ Ǥ Ǣ primafacieǯ ǤͳͲʹ Ǥ Ǣ Ǧ Ǧ ǤͳͲ͵ ǡ Ǥ ǣ ͳǤ Ǥ ʹǤ Ǥ ͵Ǥ ǡ Ǧ ǡ ǡ ǡ ǦǤ ͶǤ ǯ ǡ ǡ ǡ Ǧ Ǥ ͷǤ ͳ Ͷ ǡ ǤͳͲͶ ǡǡ Ǧ Ǥ ǡ Ͷ ǡ ǡ ͳͲʹǤ ǡ ǡ ǡ ʹ͵ȋ ƬǤǡʹͲͲȌǤ ͳͲ͵Ǥ See,e.g.,ǤǡsupraͶͲǤ ͳͲͶǤ Ǥ ǡ ǣ ǡ ͻ͵ǦͻͶȋǤʹͲͲͻȌǤ ͺͳͺ CHICAGOǦKENTLAWREVIEW ȏͺͺǣ͵ ǯǦ ǤͳͲͷ rules.1601ͲBGB Person1isobligatedtosupportPerson2 ifPerson1isindirectlineagetoPerson2 rules.1589ͲBGB Person1isindirectlineagetoPerson2 ifPerson1isanancestorofPerson2 rules.91ͲBSHG s.1601ͲBGBexcludes“Person1isobligatedtosupͲ portPerson2” if“Person1isobligatedtosupportPerson2”would causePerson1unduehardship rules.1602ͲBGB Person1isnotobligatedtosupportPerson2under s.1601ͲBGB unlessPerson2isneedy. rules.1601ͲBGBisalegalrulewith • Premises: IfdirectͲlineage(Person1,Person2) • Conclusion …thenobligatedͲtoͲsupport(Person1,Person2)is presumablytrue. • CriticalQuestions o Unlessexceptionto1601applies(e.g.,rule s.1602ͲBGB) o Assumingassumptionsof1601aremet o Assuming1601isavalidlegalrule o Unlesssomeruleexcluding1601(e.g.,rules.91Ͳ BSHG)applies o Unlesssomeconflictingruleofhigherpriority than1601applies Figure 4: “Family law” rule set and representation of rule as defeasible Lesson C2: One may attack a legal argument by attacking its asǦ sumptionsoridentifyingexceptions. Ǧ ǡ ǡ ǯ Ǥ ǡ Ǥ Ǧ Ǧ ǯǡǤͳͲ ǡ Ͷ ͳͲͷǤ See,e.g., Ƭǡsupra͵ͻǡͳͶǦǤ ͳͲǤ Ƭ ǡ supra ͵ͺǢ Ǥ ǡ Analyzing Open Source License Compatibility Issues with Carneadesǡ ʹͲͳͳ Ǥ ǯ Ǥ Ƭ ǤͷͲǡͷͳǢǤ ǡConstructingLegalArgumentswithRulesintheLegal Knowledge Interchange Format (LKIF)ǡ in ͳʹǡ ͳͺǦͻ ȋ ǡ ǡïƬǤǡʹͲͲͺȌǤ ʹͲͳ͵Ȑ DIGITALAGELEGALPRACTICE ͺͳͻ Ǥ ǤͳͲͳǦ Ǧ ͳʹDz dz ǡ ǤͳͷͺͻǦ Ǥ ͶǤǦ ǡ ǤͳͲͳǦ ǤͳͷͺͻǦ ǡǯǦ ǯ Dz dz ǤǡǡǦ Ǥ ǡ ǤͻͳǦ ǯ Ǥ Ǣ Ǥ Ǧ ǤͳͲ Ǣ ǡ Ǥand ǤǨ ǡǡͳͲͺ Ǩ ǡ Ǧ ǤǤ ǡ Dz ǫdz Ǧ Ǥ ͶǤǤͳͲͳǦ Ǧ Ǥ ǡ ǤͻͳǦ ͳ ͳ Dz Ǥdz ǡ ǤͳͲʹǦ Ǧ ʹDzǤdz ǡ ǤͳͲͳǦ ǡ presumably ȋǤǤǡ Ȍ ǡ Ǧ ǡǤ Ǧ ͳͲǤ Ƭǡsupraʹ͵Ǥ ͳͲͺǤ Id. ͺʹͲ CHICAGOǦKENTLAWREVIEW ȏͺͺǣ͵ Dz ǡdz Ǧ ǯ ǣ Ǧ ǫ ǫ ǫ ǫ Ǧ ǫ Ǧ ǡ Ǧ ǡ ǡ Ǥ Figure 5: Carneades argument diagram with defeasible inference rules ǯ Ǧ ǤͷǦ ǤͳͲͻ Ǧ ȋDzΪdz Ǧ ǯ ȌǤ ͳͲͻǤ Ƭ ǡ supra ͵ͺǢ ǡ Analyzing Open Source License Compatibility IssuesǡsupraͳͲǡͷͶǦͷͷǢ ǡConstructingLegalArguments,supraͳͲǡͳͻǤ ʹͲͳ͵Ȑ DIGITALAGELEGALPRACTICE ͺʹͳ ǡ ǣ ǤͳͲͳ ǤͳͷͺͻǤ ǡ Ǥ Ǧ ȋ ǤȌ ȋ Ǧ ȌǤ ȋ ǡ ǯ ǤȌ Figure 6: Rule’s defeasible conclusion prevented by argument based on another rule. ǡ ǡ ǡ ǯ Ǥ ǡ ǡ Ǥ Ǥͻͳ ǯ ǡ Ǥ not Ǥǡǡ ͺʹʹ CHICAGOǦKENTLAWREVIEW ȏͺͺǣ͵ Dzdzǡ ǣ Plaintiff(P) Factors ȋ undue Defendant(D) Factors ȋ hardshipȌǣ unduehardshipȌǣ ͳǤǦǦǦ Ǧ ͳǤ ǦǦǦǦǦ ʹǤǦǦǦ Ǧ ʹǤǦǦǦ ͵Ǥ Ǧ ǦǦǦǦ ǡ ǣ MuellerǣunduehardshipȓʹȔ SchmidtǣunduehardshipȓʹǡͳȔ Bauerǣunduehardshipȓʹǡͳǡ͵Ȕ ǡ ǣ Ǧ ǡ ʹǡ ǡͳǤ Figure 7: Rule’s defeasible conclusion prevented by argument based on Mueller case, which, in turn is trumped by argument based on Schmidt Case. ʹͲͳ͵Ȑ DIGITALAGELEGALPRACTICE ͺʹ͵ ǡ caseǦ based ǡ ǤͳͳͲ Mueller ǡ ʹ ǡ Ǥͻͳ ǤͳͲͳ Ǥ Schmidt ǡǡȋ Ǧ Mueller SchmidtǡʹͳȌ ǤͳͳͳSchmidt MuellerǤ Lesson C3: Legal arguments involve complex relationships among logic, rhetoric, uncertainty, and narrative. Ǧ ǡǯ Ǥ Ǥ Ǥ Ǧ ȋ Ȍǡ Ǧ Ǥ ǣ ǡ ǡǡ Ǥ Ǧ Ǥ ǦǤ Dz dzDzǦ Ǥdzǡǡ Ǧ Ǥͳͳʹ ǡ ȋǦ ȌǢ Ǧ ͳͳͲǤ supra͵ͺͳͲͻǤ ͳͳͳǤ ǡsupraͷǡͺͶǤ ͳͳʹǤ Ƭǡsupra͵ͺǡͳ͵Ǧ͵ͺǢ Ǧǡsupra͵Ǥ ͺʹͶ CHICAGOǦKENTLAWREVIEW ȏͺͺǣ͵ Ǥͳͳ͵ ǡ ǫ ǡ ǯ Ǧ ǯ Ǥ Ǧ ǤǦ Ǥ Ǧ Ǥ Ǧ ǡ Ǥ ǤǦ Ǥ Ǥ DzdzǦ Ǧ ǡ ǡ Ǥ ǣ ǤǤǤ ȏ Ȑ Ǧ Ǥ process ǡ ȏ Ȑ Ǧ ʹͲͳͷǤͳͳͶǤǤǤ ȏȐǤǤǤ Ǧ process engineeringǡ ǡ processesǤͳͳͷ Ǧ Ǧ ͳͳ͵Ǥ ƬǡsupraͺǤ ͳͳͶǤ ǤǡABlueprintforChangeǡͶͲǤǤǤͶͳǡͷͲͳȋʹͲͳ͵ȌǤ ͳͳͷǤ Id.ͷͲͷǦͲȋȌǤ ʹͲͳ͵Ȑ DIGITALAGELEGALPRACTICE ͺʹͷ Ǧ ǡ Ǧ Ǥ ǡ ǯ Ǥ DzȏȐ ȏǤǤǤȐȏȐ ǡǤǤǤ algorithmsǦ Ǧ Ǥdzͳͳ Ǧ Ǥ ǡ ǡ ǡ ǡ ǡ Ǧ Ǧ Ǥͳͳ Dz dz Ǧ Ǥ Ǧ ǯTheEndofLawyers?ǡͳͳͺǡ ȏ ȋ Ȍ ǡ ǡ ǡ ǡ ǡ Ǧ Ȑ ǡ process Ǥͳͳͻ ȏȐ Ǧ Ǧ Ǧ ǤǤǤ ȏProcessǦ ȋȌǡȐproǦ cesses Ǥǡ ȄentirelyȄ ǤǤ ǤͳʹͲ ǡǡǡ ǡ ǡǡ Ǧ ǡ ǡ ǡǦ Ǥ ǡ Dz Ǧ ͳͳǤ ͳͳǤ ͳͳͺǤ ͳͳͻǤ ͳʹͲǤ Id.ͶͺȋȌǤ Id. ǡǫȋʹͲͳͲȌǤ ǡsupraͳͳͶǡͶͻȋȌǤ Id.ͶͺȋȌǤ ͺʹ CHICAGOǦKENTLAWREVIEW ȏͺͺǣ͵ dzǤ Ǧ ǡ Ǥ Ǧ Ǧ ǡǡǡ ǡ Ǥ ǡ Ǧ ǡ ǡ Ǥ ǡ first Ǥ ǡǡ Ǧ ͳǡʹǡ͵ Ǧ Ǥ Ǧ ǡ Ǧ ȋ Ȍ ǡ Ǥ Ǥ ǡ ǡ ǡ ǡ ǡǡnotǦ Ǥǡ ǡ ǡ ǡ Ǥ ǡ ǡ ǡ Ǥǡ Ǣ ǡ Ǧ Ǥǡ Ǧ Ȁ ǡ Ǥ ǡ ǣ ʹͲͳ͵Ȑ DIGITALAGELEGALPRACTICE ͳǤ ʹǤ ͵Ǥ ͶǤ ͷǤ ͺʹ ǡ ǡ ǡǢ Ǧ ǡ Ǧ ǡ Ǣ ǡǡ ǡ Ǣ Ǣ Ǧ ǡ ǡ ǡ Ǧ ǡ Ǥ ǡ Dz dz Ǥ ǯ Ǧ ǡ Ǧ Ǥ Ǧ ǡ Ǧ Ǥ ǡ ǡ ǡ Ǧ Ǥ Ǧ Ǥ Ǧǡ ǡ ǡ Ǧ Ǥ Ǧ Ǧ Ǥ ǤǦ ǡ will Ǧ Ǥ Ǥ ǡ ǡǦǦ ͺʹͺ CHICAGOǦKENTLAWREVIEW ȏͺͺǣ͵ Ǥ Ǧ ǡ ǡ ǤǦ ǡ Ǧ ǡ Ȅ Ȅ ǡ ǡ ǡ ȋ ȌǦ Ǥ Ǧ Ǥ Ǥ Ǧ Ǥ LessonD1:LegalDigitalDocumentsTechnologiesarebasedonproǦ cessmodelsandalgorithmsǤǡ Dz Ǥdz Ǥ Ǥ Ǧ Ǥ Ǧ Ǧ Ǥ ǡ Ǧ Ǧ Ǥͳʹͳ ǯ ǡ ǡ Ǧ ǡ ǤΪ Ǧ Ǥ ǡ ǡ Ǥ ͳʹͳǤ Ƭǡsupra͵͵ǡͺͷ͵Ǥ ʹͲͳ͵Ȑ DIGITALAGELEGALPRACTICE ͺʹͻ Ǥͳʹʹ Ǥ ǡǦ ǡ Ǥͳʹ͵ Ǧ Ǧ ǡ ǡͳʹͶ ǡͳʹͷ Ǧ Ǥͳʹ Ǧ ǦǦǦ Ǥͳʹ Ǧ ͳʹͺ Ǧ Ǥ ǯ DzdzǣDz ǡ Ǧ ȏǤǡ Ǧ Ȑǡ ȏǤǤǡ ȐǤdzͳʹͻǯǦ ǣǦǦ ǡ ǡ Ǥͳ͵Ͳ Ǥ Ǧ Ǧ ǡ ǡ Ǧ Ǥ ǡ Ǥ Ǧ ǯ ǡ ǡ Ǧ Ǥǡ ǡǯǦ ͳʹʹǤ ƬòǡComputerModelsǡsupra͵Ͳǡ͵ͶǤ ͳʹ͵Ǥ ƬòǡAutomaticallyClassifyingCaseTextsǡsupraͷͺǡͳ͵ͻǦͶͲǤ ͳʹͶǤ ƬǡsupraͷͻǢǤǡsupraͷͻǤ ͳʹͷǤ ǤǡsupraͷʹǤ ͳʹǤ ǤǡsupraͷǤ ͳʹǤ ǤǡsupraͶͺǤ ͳʹͺǤ ǤǡsupraͶǡͶ͵ͶǦ͵ͷǤ ͳʹͻǤ ǡ ǡ Ƭ ǡ Automated Legal SenseǦ making:TheCentralityofRelevanceandIntentionalityǡʹͲͲͺǤ ǯ Ƭ Ǥ ʹǡ ǤͷǡʹǡavailableatǣȀȀǤ Ǥ ǤȀͻͳ͵ͳȀͳȀͻͳ͵ͳǤǤ ͳ͵ͲǤ ǤǡsupraͶǡͶͶǦͶͺǡͶͷͷǤ ͺ͵Ͳ CHICAGOǦKENTLAWREVIEW ȏͺͺǣ͵ Ǧ Ǥ Ǧ ǡ Ǥͳ͵ͳ ǡ ǡ Ǥ ǣ x x x x Dz ǣ Ǧ dz Dz dz Dz ǣ Ǧ dz Dz Ǧdz LessonD2:ProcessesunderlyinglegalDigitalDocumentsTechnoloǦ giesinvolvedifferentkindsoftextsasinputsǤ Ǣ Ǧ Ǥ not Ǥǡ ǯ ǡ Ǧ Ǥ ǡ ǡ ǡ Ǧ Ǥ Ϊ Ǥ Ǧ ǡ Ǥ Ǥ ǡ ǡ Ǧ Dzdz ȋ ǡ Dzǡdz Dzǡdz Dzǡdz et ceteraǡ ȌǤ ǡ Ǧ Ǣ ȋǡǡͳ͵ʹ Ǧ Ǧ ǡ ȌǤ ͳ͵ͳǤ ǤǡsupraͷǤ ͳ͵ʹǤ Ƭǡsupraͷ͵Ǥ ʹͲͳ͵Ȑ DIGITALAGELEGALPRACTICE ͺ͵ͳ Ǥ ȋ ȌǤ Ǧ ǡ ǡ Ǧ Ǥ Ǧ ǡ Ǧ ǡ Ǥ ǡ ǡ Ǧ ǡǯǡetceteraǤ ǡǡ Ǥͳ͵͵ LessonD3:DigitalDocumentsTechnologiesfindtextsrelevanttoleǦ gal problemsǤ Ǧ finding Ǥ ǡ ǡ Ǥ ǡǡ Ǧ ǣ Relevanceǣ ǡ DzȏȐ ȋ Ȍ ǡǤǤǤǤDz Evaluation:Dz Ǧ ȋ ȌefǦ fectiveness ǤǤǤǤȏȐ Ǧ ǣ ǤǤǤǤȏȐtopicalrelǦ evance Ǥdz Measures: Dz Ǧ ǡ ǡ ǤȋǤǤǡǦ Ǥ ǦȌ ǡ Ǥ Recall ǡpreciǦ sion Ǧ Ǥ Ǧ Ǥdzͳ͵Ͷ Ǥ ͳ͵͵Ǥ ǡsupra͵Ǥ ͳ͵ͶǤ Ǥǡsupraͳǡ͵Ͳǡ͵ʹǡ͵͵ȋȌǤ ͺ͵ʹ CHICAGOǦKENTLAWREVIEW ȏͺͺǣ͵ ǡ ǤǦ Ǧ ȋ ǡ Ȍ Ǧ ǤǦ ǡ Ǥ Ǣǡ ǡ ǯ Ǥ Ǧ Ǧ ȋ ǡ ǯ ȌǤ ǡ Ǧ ǯ Ǧ ǯ ǡ Ǧ Ǥͳ͵ͷ Ǧ Ǣ Ǥͳ͵ Ǧ ǡ ǡ Dz dz Ǧ Ǥͳ͵ǡ Ǥ Ǥ ǡ Ǥͳ͵ͺ ȏȐ Ǧ ͷΨ ͶͲǡͲͲͲ ǡ ʹͲΨ Ǥͳ͵ͻ ǡ DzȏȐ ǡ Ǧ ǡǮǮǦ Ǧ ǤǯǯͳͶͲ ͳ͵ͷǤ ͳ͵Ǥ ͳ͵Ǥ ͳ͵ͺǤ ͳ͵ͻǤ ͳͶͲǤ ǡsupraͶͻǡǦͻǤ Ǥǡsupraͳǡ͵ͷ͵Ǥ Id.͵ͷͳǤ Ƭǡsupraͷͳǡʹͻ͵ǦͻͶǤ Ǥǡsupraͳǡ͵ͶͺǦͶͻǤ Id.͵Ͷͻȋ ȌǤ ʹͲͳ͵Ȑ DIGITALAGELEGALPRACTICE ͺ͵͵ ǡ ǡǡ ǡǡ Ǧ ǡ ǯ Ǥ LessonD4:DigitalDocumentsTechnologiesandtheirprocessmodǦ els need empirical methodologies for testingǤ Ǧ Ǧ ǡ ǡ ǤǦ ǡ Ǥ Ǧ ǤͳͶͳ ǯ ǯ Ǧ Ǧ ǡͳͶʹ ǯ ǡͳͶ͵ǦǡͳͶͶǦ ǡͳͶͷ ǡͳͶǡͳͶ Ǧ Ǧ Ǥ ǤͳͶͺ Ǧ Ǧ ǤͳͶͻ ǡ Ǧ Ǥ Ǧ Ǥ ǡ Ǧ Ǧ ǡ ͳͶͳǤ See,e.g.,ǡsupraʹǢƬòǡComputerModels,supra͵ͲǢ ǡsupra͵Ǥ ͳͶʹǤ Ƭǡsupra͵ͶǤ ͳͶ͵Ǥ SeeǡsupraʹͻǡͳͻͳǦͻ͵Ǥ ͳͶͶǤ SeeƬòǡComputerModels,supra͵ͲǤ ͳͶͷǤ ǦƬǡsupra͵ͷǤ ͳͶǤ Ƭǡsupra͵ͺǢƬ Ǧǡsupra͵Ǥ ͳͶǤ Ƭǡsupra͵ͻǤ ͳͶͺǤ SeesupraͳͲͲǤ ͳͶͻǤ ǤǡsupraͳǤ ͺ͵Ͷ CHICAGOǦKENTLAWREVIEW ȏͺͺǣ͵ ͳͷͲ ǡǡ Ǥͳͷͳ ǡǦ Ǧ Ǥ ǡ Dz ǣǤǤǤ ǤǤǤǤǤǤ Ǧ ǡǤǤǤ ǤǤǤ Ǧ Ǥdzͳͷʹ ǣ ͳǤ ʹǤ ͵Ǥ ȋǤǤǡ ǯǦ ȌǢͳͷ͵ ȋǤǤǡ ǡ Ǧ ȌǢͳͷͶ ȋǤǤǡ ǦȌǤͳͷͷ ǡ ǡ Ǥͳͷ ǡ Ǧ ǡǦ Ǧ ǡ Ǧ ǯ Ǧ ǡ Ǧ ǡ Ǧ Ǥ LessonD5:Findingtextsrelevanttolegalproblemsisdifferentfrom applying relevant texts to solve legal problemsǤ ǡ Ǧ ͳͷͲǤ ƬǡsupraͷͳǤ ͳͷͳǤ Ǥǡsupraͳǡ͵Ǥ ͳͷʹǤ Id.͵ͲǤ ͳͷ͵Ǥ Id. ͳͷͶǤ Id.͵͵Ǥ ͳͷͷǤ Id.͵ͷǤ ͳͷǤ Id.͵ͲǤDzǡ Ǧ Ǣǡ Ǣ ǡ ǢǤdz ʹͲͳ͵Ȑ DIGITALAGELEGALPRACTICE ͺ͵ͷ ǡ Ǥ ǫ ǡǡ ǡ Ǧ ǡ ǡ ǡ ǫ ǡ ǯ Ǥ Ǧ ǯǡ DzǦ dz Ǥ ǡ findingǤ Ǧ ǡ applyingǤ ǡ Ǧ Ǥ ȋǡǦ Ȍ Ǧ Ǣ Ǥ ǡ ǡ ǡ Ǥ ǡǡ ǡ ͳǡ ǤǡǡǦ ǡ ǡ ǡetceteraǤǡǦ ͳͷΪͳͷͺǡ Ǥ ǡ Ǧ Ǥ LessonD6:Computationalmodelsoflegalreasoningcanbeabridge between legal texts and legal problem solving. Ǧ Ǧ ͳͷǤ Ƭǡsupraͷ͵Ǥ ͳͷͺǤ SeeƬòǡAutomaticallyClassifyingCaseTexts,supraͷͺǤ ͺ͵ CHICAGOǦKENTLAWREVIEW ȏͺͺǣ͵ Ǥ ǡǡ Ǧ Ǧ ȋǦ Ȍ Ǧ Ǥ Ǧ Ǣ ǡ ǦǤͳͷͻ ǡ Ǧ ȋ ǡ Ǧ ȌǤ ǡ Ǥ ͳͲ Ǧ ȋǤǤǡǡǡ ǡ ǡ ǡ ȌǤ Ǥͳͳ ǡ ǡ Dz Ǧ ǡdz Dzdz ǣ ȋ Ȍ α Dz dzǡ α Dz dzǡ αDz dzǡ αDz dzǤ Ǥǡ Ǥ ǡ Ǥǡ ǡǦ ȋ ǡ ǡ Ȍ ǡǤ Ǧ Ǥ ǡ ͳͷͻǤ Id.ͳ͵ͳǦ͵ʹǤ ͳͲǤ ǤǡsupraͷͻǤ ͳͳǤ ǤǡsupraͶͺǤ ʹͲͳ͵Ȑ DIGITALAGELEGALPRACTICE ͺ͵ Ǧ ͳʹ Ǧ Ǥͳ͵ ȋǡȌǦ Ǥ Ǧ Ǧ ǡ ǡ Ǥ ǡ Ǧ ȋ Dz dzȌ ǡ Ǧ ȋ Dz ǡdzDzdzȌ Ǥ ǡ Ǧ ǡ ǯ Ǧ Ǥǡ ǡ ǤͳͶǡǦ all Ȅǡ ǡǡ ǡ Ǥ ǡ Ǥ ͳͷ ǡǡ Ǧ Ǧ Ǥ ǡǡ ǡ Ǥ Ǧ Ǥǡ Ǧ ͳʹǤ ͳ͵Ǥ ͳͶǤ ͳͷǤ ǤǡsupraǡǤ ƬǡsupraǤ Seeǡ Ƭ ǡsupraͳǤ ƬǡsupraͲǢǡsupraʹǤ ͺ͵ͺ CHICAGOǦKENTLAWREVIEW ȏͺͺǣ͵ Ǥ Ǧ ǡ Ǥ Ǥ Ǥ Ǧ ǡ ǡ Ǥ ǡ ǯ Ǥ ǡ Ǧ ǡ ǡ ǡ ǡ Ǥcan Ǧ ǡǡ Ǥ ǡ Ǥ ǡ Ǥͳ Ǥ ǣ Ǣͳ Ǧ Ǧ Ǣͳͺ Ǣͳͻ Ǧ ǤͳͲ ͳǤ ͳǤ ͳͺǤ ͳͻǤ ͳͲǤ Seesupra Ǥ Seesupraͳͳ͵ Ǥ SeesupraͳͶͻ Ǥ Id. Seesupraͳ͵ Ǥ ʹͲͳ͵Ȑ DIGITALAGELEGALPRACTICE ͺ͵ͻ Ǧ Ǥ SampleSyllabusforAIandLawSeminar PartOne:ComputationalModelsofReasoningwithLegalRulesand Cases I.IntroductiontoComputationalModelsofLegalReasoning x x x Ǥǡ ǣ ǡ ͻͻ Ǥ Ǥ ͳͻͷ ȋͳͻͻͲȌǤ Ǥ Ƭ Ǥ ǡ ȋͳͻͺͳȌǤ ǡ ȋͳͻͺȌǤ II.LogicalModelsofStatutoryReasoning x x x x Ǥ ǡ ǡ Ǥ ǡ ǡǤƬǤǤǡTheBritishNationality ActasaLogicProgram,ʹͻǤ͵ͲȋͳͻͺȌǤ Ǧ Ƭ ǡ Exploiting IsomorǦ phism: Development of a KBS to Support British Coal InsurǦ anceClaimsǡͳͻͻͳǤǯǤ ƬǤʹǤ ǤƬǤǡNormalizedLegalDraftǦ ing and the Query Method, ʹͻ Ǥ Ǥ ͵ͺͲ ȋͳͻǦ ͳͻͺȌǤ Ƭ ǡ Obstacles to the DevelopǦ mentofLogicǦBasedModelsofLegalReasoning,in ͳͺ͵ǦʹͳͶ ȋ Ǥǡ ͳͻͺȌǤ III.CaseǦBasedModelsofLegalReasoning x x Ǥ ǡAnImplementationofǤ Ǧ ǡ ͳͻͻͷ Ǥ ǯ Ǥ ƬǤʹǤ Ǥ ǡ Reasoning with Cases and Hypotheticals in HYPO,͵ͶǯǤǦǤǤͷ͵ȋͳͻͻͳȌǤ ͺͶͲ CHICAGOǦKENTLAWREVIEW x ȏͺͺǣ͵ ǤǡBuildingExplanationsfromRulesandStrucǦ turedCasesǡ͵ͶǯǤǦǤǤͻȋͳͻͻͳȌǤ Ǥ x x ǡ Using Background Knowledge in CaseǦ BasedLegal Reasoning:AComputationalModelandanInǦ telligent Learning Environmentǡ ͳͷͲ ͳͺ͵ȋʹͲͲ͵ȌǤ ǤƬòǡComputerModels forLegalPredictionǡͶ͵ͲͻȋʹͲͲȌǤ V.ModelsIntegratingCases,Statutes,Rules,Concepts,andValues x x x x Ǥ Ƭ Ǥ ǡ CABARET: Statutory Interpretation in a Hybrid Architectureǡ ͵Ͷ ǯ Ǥ Ǧ ǤǤͺ͵ͻȋͳͻͻͳȌǤ ǤƬǡRepresentingTeleological Structure in CaseǦBased Legal Reasoning: The Missing Linkǡ ͳͻͻ͵ Ǥ ǯ Ǥ ƬǤͷͲǤ Ǧ Ƭ ǡ A Model of Legal ReasoningwithCasesIncorporatingTheoriesandValuesǡͳͷͲ ͻȋʹͲͲ͵Ȍ. Ǥ ǡExtensionallyDefiningPrinciplesandCasǦ es in Ethics:An AI Modelǡ ͳͷͲ ͳͶͷ ȋʹͲͲ͵ȌǤ PartTwo:DefeasibleLegalReasoningwithArgumentSchemes VI.AssessingLegalClaimswithArgumentSchema x x x x Ǥ ƬǡLegalReasoningwith Argumentation Schemesǡ ʹͲͲͻ Ǥ ǯ Ǥ ƬǤͳ͵Ǥ Ƭ Ǧǡ Argumentation and StandardsofProofǡʹͲͲǤǯǤ ƬǤͳͲǤ ƬǤǡFacilitatingCaseComǦ parison Using Value Judgments and Intermediate Legal ConǦ ceptsǡʹͲͳͳǤ ǯ Ǥ ƬǤͳͳǤ Ǥǡ ǡǤƬ ǡAFrameworkfortheExtractionandModelingofFactǦ Finding Reasoning from Legal Decisions: Lessons from the Vaccine/InjuryProjectCorpusǡͳͻ ƬǤ ʹͻͳȋʹͲͳͳȌǤ ʹͲͳ͵Ȑ DIGITALAGELEGALPRACTICE ͺͶͳ VII.RepresentingLegalConceptsandCaseKnowledgeinOntoloǦ gies x x x x x ǤƬòǡAPredictiveRolefor Intermediate Legal Conceptsǡ Ǥ Ǥ Ǥ ƬǤǤͳͷ͵ȋʹͲͲ͵ȌǤ Ǥ ǡ Ontological Requirements for Analogical, Teleological, and Hypothetical Legal Reasoningǡ ʹͲͲͻ Ǥ ǯǤ ƬǤͳǤ ǤǤ ǦƬǤǤǡOntologiesin Legal Information Systems; The Need for Explicit SpecificaǦ tionsof Domain Conceptualisationsǡ ͳͻͻ Ǥ ǯǤ ƬǤͳ͵ʹǤ ǡƬǡLegalOnǦ tologies in Knowledge Engineering and Information ManǦ agementǡͳʹ ƬǤʹͶͳȋʹͲͲͶȌǤ ǡ ǡ Ƭ ǡ Integrating a BottomǦUp and TopǦDown Methodology for Building Semantic Resources for the MultiǦ lingualLegalDomainǡin ǣ ͻͷ ȋ ǡǡƬ ǤǡʹͲͳͲȌǤ PartThree:LegalInformationRetrieval,InformationExtraction,and TextProcessing VIII.HowFullǦTextLegalInformationRetrievalWorks x x x ǡ Text Retrieval in the Legal Worldǡ ͵ ƬǤͷȋͳͻͻͷȌǤ Ǥ Ƭ Ǥ Ǥ ǡ An Evaluation ofRetrieval EfǦ fectiveness for a FullǦText DocumentǦRetrieval Systemǡ ʹͺ ǤʹͺͻȋͳͻͺͷȌǤ ǡ Bayesian Networks Without Tearsǡ ͳʹ ǡͳͻͻͳǡͷͲǤ IX.CombiningLegalIRandAItoAnalyzeLegalClaims x x x Ǥ Ƭ Ǥ ǡ Finding Legally ReleǦ vantPassagesinCaseOpinionsǡͳͻͻǤ ǯ Ǥ ƬǤ͵ͻǤ ǤǡǤƬǤǡImprovingLegalInǦ formation Retrieval Using an Ontological Frameworkǡ ͳ ƬǤͳͲͳȋʹͲͲͻȌǤ ǡ Ǧǡ Ƭ ǡInformationExtractionfromCaseLawandRetrieval ofPriorCasesǡͳͷͲ ʹ͵ͻȋʹͲͲ͵ȌǤ ͺͶʹ CHICAGOǦKENTLAWREVIEW x x x ȏͺͺǣ͵ ǤƬǤǡDetectingChange in Legal Conceptsǡ ͳͻͻͷ Ǥ ǯ Ǥ ƬǤͳʹǤ ǤƬ ǤǡAConnectionistandSymǦ bolicHybridforImprovingLegalResearchǡ͵ͷǯ ǤǦ ǤǤͳȋͳͻͻͳȌǤ ǡƬǡBankXX: Supporting Legal Arguments through Heuristic Retrievalǡ Ͷ ƬǤͳȋͳͻͻȌǤ X.LegalInformationExtractionandTextProcessing x x x ǤƬòǡAutomaticallyClasǦ sifying Case Texts and Predicting Outcomesǡ ͳ ƬǤͳʹͷȋʹͲͲͻȌǤ Ƭ ǡ Automatic ClassifiǦ cation of Provisions in Legislative Textsǡ ͳͷ ƬǤͳȋʹͲͲȌǤ ǡ Ƭ ǡ Machine Learning Versus Knowledge Based Classification of Legal Textsǡ ʹͲͳͲ Ǥ Ǧ Ǥ Ǥ ƬǤǤͺǤ XI.AIandLawToolsforEǦDiscovery:ObtainingEvidenceRelevant toLawyers’HypothesesaboutClaimsandDocuments x x x x x x Ƭ ǡ Ƭ Ǧ Ǧ ǡ ͳͺ ƬǤ͵ͳͳȋʹͲͳͲȌǤ ǤǡǤǡ ǡǤǦ Ƭǡ Ǧ ǡͳͺ ƬǤ͵ͶȋʹͲͳͲȌǤ ǡǦǦ Ǧ ǡ ͳͺ Ƭ Ǥ Ͷͳ͵ ȋʹͲͳͲȌǤ ǡ Ǥ Ƭ ǡ Ǧ Ǧ ǡ ͳͺ ƬǤͶ͵ͳȋʹͲͳͲȌǤ ǡ ǯǡ Ƭ Ǧ ǡ Ǧ ǡ ͳͺ Ƭ Ǥ Ͷͷͻ ȋʹͲͳͲȌǤ Ǥ ǡ ǣ ǡ ǡ Ǧ ǡͳͺ ƬǤͶͺͳȋʹͲͳͲȌǤ ʹͲͳ͵Ȑ DIGITALAGELEGALPRACTICE ͺͶ͵ PartFour:TheFutureofAIandLaw:BridgingComputationalModels andLegalTexts XII.NearTermDevelopmentsinAIandLaw x x x x x x ǡ ǡǦǡǡ ǡ Ǥ ǡ ǡ Ǥ ǡ ǡ ǡ Ƭ ǡBuildingWatson:AnOverviewoftheDeepQAProjectǡ ͵ͳ ǡʹͲͳͲǡͷͻǤ ǤƬǤǡ Ǧ ǣ Ǧ ǡ ȋ Ǧ ȌȋȌǤ Ƭ ǡRule Based Business ProǦ cess Complianceǡ ͺͶ Ǥ ʹͲͳʹ̷ ǡ ǯ Ǥ Ǥ ͷ ȋʹͲͳʹȌǡ available at ǣȀȀ ǦǤȀǦ ͺͶȀͳǤǤ ƬǤǡASurveyofUncertainǦ ties and their Consequencesǡ in ͳ ȋ ǤǡʹͲͳʹȌǤ ǡ ǣ Ǧ Ǣ ǡ ȋ Ǥ ͷǡ ʹͲͳͲȌǡ available atǣȀȀǤ Ǥ Ȁ̴ Ȁ ̴Ȁ̴̴̴ǤǤ ȋ ǡ ǤȌ Ǥ ǡƬǤǡADeclaraǦ tiveApproachtoBusinessRulesinContracts:CourteousLogic Programs in XMLǡ ͳͻͻͻ Ǥ Ǥ Ǥ Addenda XIII.IntelligentTutoringSystemsinLawandEthicalReasoning x x ǡ Ǧ ǣ ǡ ͳͷͲ ͳͺ͵ ȋʹͲͲ͵ȌǤ ǤǡǦ ǡ ǣ Ǧ ͷ ȋ Ǥ ǡ Ǥ ƬǤǡǤǡʹͲͲ͵ȌǤ ͺͶͶ CHICAGOǦKENTLAWREVIEW x x x x ȏͺͺǣ͵ Ǥ ǡ Ǧ ǡͳ ƬǤ͵ʹͳ ȋʹͲͲͻȌǤ Ǥǡ ǡͳͲ Ǥ Ǥ ǤͻȋʹͲͲͻȌǤ Ƭ ǡǦ Ǧ ǡ ʹͲͳͳ Ǥ Ǧ Ǥ Ǥ Ƭ Ǥ ǤͳǤ ǡ Ǥ Ƭ Ǥ ǡ ǡʹͲͳʹǤ Ǧ ǤǤ ƬǤǤͺͳǤ XIV.LegalandEthicalIssuesRelatedtoAIandLawPrograms x x x x Ƭ ǡǦ ͳͳͻȋʹͲͳͳȌǤ Ǥ ǡ The Use of Legal Software by NonǦLawyers andthePerilsofUnauthorisedPracticeofLawChargesinthe UnitedStates:AReviewofJaysonReynosoDecisionǡͳͺǦ ƬǤʹͺͷȋʹͲͳͲȌǤ Ǥ Ǥ ǡ Intelligent Agents and Liability: Is It a Doctrinal Problem or Merely A Problem of Explanation?ǡ ͳͺ ƬǤͳͲ͵ȋʹͲͳͲȌǤ ǡIntellectualPropertyRightsinVirtualEnǦ vironments: Considering the Rights of Owners, Programmers andVirtualAvatarsǡ͵ͻǤǤͶͻȋʹͲͲȌǤ
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