Human-AI Teaming State-of-the-Art and Research Needs (2022) / Chapter Skim
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6 Human-AI Team Interaction
6 Human-AI Team Interaction
Pages 41-48
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From page 41... ...
Considerable work has been done describing the effects of LOA on the human workload, SA, and performance of human users, showing that the aspect of task performance being automated can impact human performance.
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From page 42... ...
AI efforts directed at improving human SA and understanding of events, particularly integrations from large, heterogeneous datasets, will be most useful and least likely to suffer from negative OOTL effects. AI efforts for improving decision making may be useful if combined with information presentations that allow people to easily understand the basis
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From page 43... ...
. AI that executes tasks to human specifications will reduce some human workload but may either demand additional monitoring to ensure reliable performance or may produce OOTL effects when not performing reliably.
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Research Needs The committee recommends addressing three major research objectives for improving human-AI interaction across LOAs. Research Objective 6-1: Human-AI Team Task Sharing.
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Research Needs The committee recommends addressing two major research objectives for improving human-AI teaming using a flexible automation approach. Research Objective 6-4: Flexible Autonomy Transition Support.
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From page 46... ...
Key Challenges and Research Gaps The committee finds two major research gaps related to GOC, in the following areas: • Effects of Playbook control on SA and OOTL; and • Applicability of Playbook control to new applications relevant to multi-domain operations. Research Needs The committee recommends addressing two major research objectives related to the use of GOC as a method for integrating human-AI teams.
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From page 47... ...
Key Challenges and Research Gaps The committee finds three key research gaps in the area of human-AI team interaction, including • Prediction of emergent behaviors in human-AI team interaction; • Effects of human-AI team interaction design on skill retention, training requirements, job satisfaction, and resilience; and • Predictive models of human-AI team performance in both routine and failure conditions. Research Needs The committee recommends addressing two research objectives for improving human-AI interaction.
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From page 48... ...
The LOA and the ways that LOA assignments can change over time present a key design decision for human-AI teams. Research is needed to better support flexible automation, to support low-workload GOC approaches such as Playbook control, and to explore additional features of human-AI interaction in team settings.
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