The Impact of Explanation Facilities on User Acceptance of Expert Systems Advice.

Author List: Ye, L.Richardye; Johnson, Paul;

MIS Quarterly, 1995, Volume 19, Issue 2, Page 157-172.

Providing explanations for recommended actions is deemed one of the most important capabilities of expert systems (ES). There is little empirical evidence, however, that explanation facilities indeed influence user confidence in, and acceptance of ES-based decisions and recommendations. This paper investigates the impact of ES explanations on changes in user beliefs toward ES-generated conclusions. Grounded on a theoretical model of argument, three alternative types of ES explanations-trace, justification, and strategy-were provided in a simulated diagnostic expert system performing auditing tasks. Twenty practicing auditors evaluated the outputs of the system in a laboratory setting. The results indicate that explanation facilities can make ES-generated advice more acceptable to users and that justification is the most effective type of explanation to bring about changes in user attitudes toward the system. These findings are expected to be generalizable to application domains that exhibit similar characteristics to those of auditing: domains in which decision making tends to be judgmental and yet highly consequential, and the correctness or validity of such decisions cannot be readily verified.

Keywords: Auditing; expert systems; explanation facilities. justification; user acceptance

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List of Topics

#183	0.331	explanations explanation bias use kbs biases facilities cognitive making judgment decisions likely decision important prior judgments feedback types difficult lead
#253	0.151	user involvement development users satisfaction systems relationship specific results successful process attitude participative implementation effective application authors suggested user's contingency
#129	0.136	expert systems knowledge knowledge-based human intelligent experts paper problem acquisition base used expertise intelligence domain inductive rules machine artificial task
#51	0.087	results study research experiment experiments influence implications conducted laboratory field different indicate impact effectiveness future participants evidence test controlled involving
#94	0.063	effort users advice ras trade-off recommendation agents difficulty decision make acceptance product loss trade-offs context perceived influence laboratory reasons consumers
#127	0.059	systems information research theory implications practice discussed findings field paper practitioners role general important key grounded researchers domain new identified