Date of Original Version

2001

Type

Conference Proceeding

Rights Management

Copyright © 2001 by the Association for Computing Machinery, Inc. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Publications Dept., ACM, Inc., fax +1 (212) 869-0481, or permissions@acm.org. © ACM, 2001. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in the Proceedings of the 8th international conference on Artificial intelligence and law {1-58113-368-5 (2001)} http://doi.acm.org/10.1145/383535.383539

Abstract or Description

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.

Comments

Copyright © 2001 by the Association for Computing Machinery, Inc. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Publications Dept., ACM, Inc., fax +1 (212) 869-0481, or permissions@acm.org. © ACM, 2001. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in the Proceedings of the 8th international conference on Artificial intelligence and law {1-58113-368-5 (2001)} http://doi.acm.org/10.1145/383535.383539

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