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A model for types and levels of human interaction with automation

Published: 01 May 2000 Publication History

Abstract

We outline a model for types and levels of automation that provides a framework and an objective basis for deciding which system functions should be automated and to what extent. Appropriate selection is important because automation does not merely supplant but changes human activity and can impose new coordination demands on the human operator. We propose that automation can be applied to four broad classes of functions: 1) information acquisition; 2) information analysis; 3) decision and action selection; and 4) action implementation. Within each of these types, automation can be applied across a continuum of levels from low to high, i.e., from fully manual to fully automatic. A particular system can involve automation of all four types at different levels. The human performance consequences of particular types and levels of automation constitute primary evaluative criteria for automation design using our model. Secondary evaluative criteria include automation reliability and the costs of decision/action consequences, among others. Examples of recommended types and levels of automation are provided to illustrate the application of the model to automation design

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cover image IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans  Volume 30, Issue 3
May 2000
159 pages

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IEEE Press

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Published: 01 May 2000

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Cited By

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  • (2024)Investigating the Impact of Control in AI-Assisted Decision-Making - An Experimental StudyProceedings of Mensch und Computer 202410.1145/3670653.3677476(419-423)Online publication date: 1-Sep-2024
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