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Expert systems should be more accurate than human experts - Evaluation procedures from human judgment and decisionmakingTwo procedures for the evaluation of the performance of expert systems are illustrated: one procedure evaluates predictive accuracy; the other procedure is complementary in that it uncovers the factors that contribute to predictive accuracy. Using these procedures, it is argued that expert systems should be more accurate than human experts in two senses. One sense is that expert systems must be more accurate to be cost-effective. Previous research is reviewed and original results are presented which show that simple statistical models typically perform better than human experts for the task of combining evidence from a given set of information sources. The results also suggest the second sense in which expert systems should be more accurate than human experts. They reveal that expert systems should share factors that contribute to human accuracy, but not factors that detract from human accuracy. Thus the thesis is that one should both require and expect systems to be more accurate than humans.
Document ID
19890065789
Acquisition Source
Legacy CDMS
Document Type
Reprint (Version printed in journal)
External Source(s)
Authors
Levi, Keith
(Honeywell Systems and Research Center Minneapolis, MN, United States)
Date Acquired
August 14, 2013
Publication Date
June 1, 1989
Publication Information
Publication: IEEE Transactions on Systems, Man, and Cybernetics
Volume: 19
ISSN: 0018-9472
Subject Category
Cybernetics
Accession Number
89A53160
Distribution Limits
Public
Copyright
Other

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