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Designing Intelligent Systems to Handle System Failures: Enhancing Explanatory Power With Less Restrictive User Interfaces and Deep Explanations.

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Title: Designing Intelligent Systems to Handle System Failures: Enhancing Explanatory Power With Less Restrictive User Interfaces and Deep Explanations.
Authors: Nakatsu, Robbie T., Benbasat, Izak
Source: International Journal of Human-Computer Interaction. 2006, Vol. 21 Issue 1, p55-72. 18p. 1 Illustration, 3 Diagrams, 4 Charts, 2 Graphs.
Subjects: Expert systems, Computer system failures, Computer system failure prevention, Artificial intelligence in engineering, Transparency (Optics), Ferromagnetic materials, Magnetic domain
Abstract: This research empirically investigates the design choices that can be made to facilitate problem solving when intelligent systems fail. One way is to provide deep explanations, which are explanations that justify system actions. Another way is to manipulate system restrictiveness of the user interface. An experiment was conducted to investigate the effectiveness of deep explanation support, as well as manipulations of system restrictiveness. Results suggest that the less restrictive system was more effective for problem-solving situations where system failure occurred. In addition, deep explanations were found to be somewhat helpful in system understanding, and this, in turn, led to improved problem-solving performance. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Human-Computer Interaction is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Designing Intelligent Systems to Handle System Failures: Enhancing Explanatory Power With Less Restrictive User Interfaces and Deep Explanations.
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  Data: <searchLink fieldCode="AR" term="%22Nakatsu%2C+Robbie+T%2E%22">Nakatsu, Robbie T.</searchLink><br /><searchLink fieldCode="AR" term="%22Benbasat%2C+Izak%22">Benbasat, Izak</searchLink>
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Human-Computer+Interaction%22">International Journal of Human-Computer Interaction</searchLink>. 2006, Vol. 21 Issue 1, p55-72. 18p. 1 Illustration, 3 Diagrams, 4 Charts, 2 Graphs.
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  Data: <searchLink fieldCode="DE" term="%22Expert+systems%22">Expert systems</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+system+failures%22">Computer system failures</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+system+failure+prevention%22">Computer system failure prevention</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence+in+engineering%22">Artificial intelligence in engineering</searchLink><br /><searchLink fieldCode="DE" term="%22Transparency+%28Optics%29%22">Transparency (Optics)</searchLink><br /><searchLink fieldCode="DE" term="%22Ferromagnetic+materials%22">Ferromagnetic materials</searchLink><br /><searchLink fieldCode="DE" term="%22Magnetic+domain%22">Magnetic domain</searchLink>
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  Data: This research empirically investigates the design choices that can be made to facilitate problem solving when intelligent systems fail. One way is to provide deep explanations, which are explanations that justify system actions. Another way is to manipulate system restrictiveness of the user interface. An experiment was conducted to investigate the effectiveness of deep explanation support, as well as manipulations of system restrictiveness. Results suggest that the less restrictive system was more effective for problem-solving situations where system failure occurred. In addition, deep explanations were found to be somewhat helpful in system understanding, and this, in turn, led to improved problem-solving performance. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of International Journal of Human-Computer Interaction is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1207/s15327590ijhc2101_4
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        Text: English
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      – SubjectFull: Expert systems
        Type: general
      – SubjectFull: Computer system failures
        Type: general
      – SubjectFull: Computer system failure prevention
        Type: general
      – SubjectFull: Artificial intelligence in engineering
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      – SubjectFull: Transparency (Optics)
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      – SubjectFull: Ferromagnetic materials
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      – SubjectFull: Magnetic domain
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      – TitleFull: Designing Intelligent Systems to Handle System Failures: Enhancing Explanatory Power With Less Restrictive User Interfaces and Deep Explanations.
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              Text: 2006
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