A CBR knowledge representation for practical ethics

Abstract

TRUTH-TELLER, a program for testing a Case-Based Reasoning (CBR) knowledge representation in practical ethics, compares cases presenting ethical dilemmas about whether to tell the truth. Its comparisons list ethically relevant similarities and differences (i.e., reasons for telling or not telling the truth which apply to both cases, and reasons which apply more strongly in one case than another or which apply only to one case). The program reasons about reasons in generating context-sensitive comparisons. The reasons may invoke ethical principles or selfish considerations. We describe a knowledge representation for this practical ethical domain including representations for reasons and principles, truth telling episodes, contextually important scenarios, and comparison rules. In a preliminary evaluation, a professional ethicist scored the program's output for randomly-selected pairs of cases. The work contributes to AI CBR efforts to integrate general principles and context-sensitive information in symbolically assessing case similarity and to model comparing problems to paradigmatic cases. It also furthers research on cognitive and philosophical models of ethical judgement and decision-making.

This Work is supported by The Andrew W. Mellon Foundation. We are grateful to Athena Beldecos for her research on casuistic models and data collection of case comparison protocols. We are also grateful to Ken Schaffner, University Professor of Medical Humanities, George Washington University, for participating in our preliminary evaluation. Vincent Aleven has given us good advice in designing our evaluation.