Extensionally Defining Principles and Cases in Ethics: an AI model

Principles are abstract rules intended to guide decision-makers in making normative judgments in domains like the law, politics, and ethics. It is difficult, however, if not impossible to define principles in an intensional manner so that they may be applied deductively. The problem is the gap between the abstract, open-textured principles and concrete facts. On the other hand, when expert decision-makers rationalize their conclusions in specific cases, they often link principles to the specific facts of the cases. In effect, these expert-defined associations between principles and facts provide extensional definitions of the principles. The experts operationalize the abstract principles by linking them to the facts. This paper discusses research in which the following hypothesis was empirically tested: extensionally defined principles, as well as cited past cases, can help in predicting the principles and cases that might be relevant in the analysis of new cases. To investigate this phenomenon computationally, a large set of professional ethics cases was analyzed and a computational model called SIROCCO, a system for retrieving principles and past cases, was constructed. Empirical evidence is presented that the operationalization information contained in extensionally defined principles can be leveraged to predict the principles and past cases that are relevant to new problem situations. This is shown through an ablation experiment, comparing SIROCCO to a version of itself that does not employ operationalization information. Further, it is shown that SIROCCO’s extensionally defined principles and case citations help it to outperform a full-text retrieval program that does not employ such information.