Building explanations from rules and structured cases
International Journal of Man-Machine Studies - AI and legal reasoning. Part 1
Training algorithms for linear text classifiers
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Maintaining knowledge about temporal intervals
Communications of the ACM
Modeling Legal Arguments: Reasoning with Cases and Hypotheticals
Modeling Legal Arguments: Reasoning with Cases and Hypotheticals
Assessing Relevance with Extensionally Defined Principles and Cases
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Teaching case-based argumentation through a model and examples
Teaching case-based argumentation through a model and examples
Assessing the relevance of cases and principles using operationalization techniques
Assessing the relevance of cases and principles using operationalization techniques
Proceedings of the 13th International Conference on Artificial Intelligence and Law
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This paper describes how we used an AI model for retrieving ethics cases to investigate empirically the epistemological contributions of a decision-makers' citing cases and code provisions in justifying decisions. In practical ethics, like law, it is impossible to define abstract principles intensionally so that they may be applied deductively. After investigating hundreds of professional ethics case opinions, we hypothesized that the decision-makers' explanations extensionally defined principles over time, in effect, operationalizing them. We constructed SIROCCO, a system for retrieving principles and past ethics cases. We used this computational model to conduct an ablation experiment concerning a core set of operationalization techniques. This paper presents empirical evidence that the operationalization information supports predictions of the relevant principles and past cases more accurately than competing approaches that do not use such information.