Should We Just Let the Machines Do It? The Benefit and Cost of Action Recommendation and Action Implementation Automation

Abstract

Objective: To examine the effects of action recommendation and action implementation automation on performance, workload, situation awareness (SA), detection of automation failure, and return-to-manual performance in a submarine track management task. Background: Theory and meta-analytic evidence suggest that with increasing degrees of automation (DOA), operator performance improves and workload decreases, but SA and return-to-manual performance declines. Method: Participants monitored the location and heading of contacts in order to classify them, mark their closest point of approach (CPA), and dive when necessary. Participants were assigned either no automation, action recommendation automation, or action implementation automation. An automation failure occurred late in the task, whereby the automation provided incorrect classification advice or implemented incorrect classification actions. Results: Compared to no automation, action recommendation automation benefited automated task performance and lowered workload, but cost nonautomated task performance. Action implementation automation resulted in perfect automated task performance (by default) and lowered workload, with no costs to nonautomated task performance, SA, or return-to-manual performance compared to no automation. However, participants provided action implementation automation were less likely to detect the automation failure compared to those provided action recommendations, and made less accurate classifications immediately after the automation failure, compared to those provided no automation. Conclusion: Action implementation automation produced the anticipated benefits but also caused poorer automation failure detection. Application: While action implementation automation may be effective for some task contexts, system designers should be aware that operators may be less likely to detect automation failures and that performance may suffer until such failures are detected.