EXPLANATIONS FROM INTELLIGENT SYSTEMS: THEORETICAL FOUNDATIONS AND IMPLICATIONS FOR PRACTICE.

Author List: Gregor, Shirley; Benbasat, Izak;
MIS Quarterly, 1999, Volume 23, Issue 4, Page 497-530.
Information systems with an "intelligent" or "knowledge" component are now prevalent and include knowledge-based systems, decision support systems, intelligent agents, and knowledge management systems. These systems are in principle capable of explaining their reasoning or justifying their behavior. There appears to be a lack of under, standing, however, of the benefits that can flow from explanation use, and how an explanation function should be constructed. Work with newer types of intelligent systems and help functions for everyday systems, such as word-processors, appears in many cases to neglect lessons learned in the past. This paper attempts to rectify this situation by drawing together the considerable body of work on the nature and use of explanations. Empirical studies, mainly with knowledge-based systems, are reviewed and linked to a sound theoretical base. The theoretical base combines a cognitive effort perspective, cognitive learning theory, and Toulmin's model of argumentation. Conclusions drawn from the review have both practical and theoretical significance. Explanations are important to users in a number of circumstances--when the user perceives an anomaly, when they want to learn, or when they need a specific piece of knowledge to participate properly in problem solving. Explanations, when suitably designed, have been shown to improve performance and learning and result in more positive user perceptions of a system. The design is important, however, because it appears that explanations will not be used if the user has to exert "too much" effort to get them. Explanations should be provided automatically if this can be done relatively unobtrusively, or by hypertext links, and should be context-specific rather than generic. Explanations that conform to Toulmin's model of argumentation, in that they provide adequate justification for the knowledge offered, should be more persuasive and lead to greater trust, agreement, satisfaction, and acceptance--of the explanation and possibly also of the system as a whole.
Keywords: cognitive effort; cognitive learning; decision support systems; expert systems; Explanation use; explanations; intelligent agents; intelligent systems; knowledge-based systems
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#183 0.211 explanations explanation bias use kbs biases facilities cognitive making judgment decisions likely decision important prior judgments feedback types difficult lead
#129 0.175 expert systems knowledge knowledge-based human intelligent experts paper problem acquisition base used expertise intelligence domain inductive rules machine artificial task
#240 0.101 systems information management development presented function article discussed model personnel general organization described presents finally computer-based role examined functional components
#26 0.080 business large organizations using work changing rapidly make today's available designed need increasingly recent manage years activity important allow achieve
#57 0.066 decision support systems making design models group makers integrated article delivery representation portfolio include selection effective claims decisions rationale various
#94 0.058 effort users advice ras trade-off recommendation agents difficulty decision make acceptance product loss trade-offs context perceived influence laboratory reasons consumers
#110 0.053 theory theories theoretical paper new understanding work practical explain empirical contribution phenomenon literature second implications different building based insights need
#95 0.051 learning mental conceptual new learn situated development working assumptions improve ess existing investigates capture advanced proposes types context building acquisition