Mutually beneficial decision making in Human-AI teams: Understanding soldier’s perception and expectations from AI teammates in human-AI teams

Sawant, Sarvesh; Mallick, Rohit; McNeese, Nathan J.; Madathil, Kapil Chalil

doi:10.1177/1071181322661355

Cited by 4 publications

(1 citation statement)

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“…This task is highly risky as IEDs pose one of the deadliest threats to military personnel, accounting for more than 50% of US Army casualties overseas (Wilson, 2007). By evaluating soldiers' perceptions of AI agents as teammates, we can gain insights into their expectations and design AI teams that facilitate effective bidirectional information transfer (Sawant et al, 2022). Additionally, effective communication and information transfer are crucial for team success.…”

Section: Introductionmentioning

confidence: 99%

Human-AI teams in complex military operations: Soldiers’ perception of intelligent AI agents as teammates in human-AI teams

Sawant,

Brady,

Mallick

et al. 2023

Proceedings of the Human Factors and Ergonomics Society Annual

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Military decision-making frequently involves complex problems in non-routine situations with minimal rule-based or automated solutions. As human information processing capabilities are limited, processing the required scale of information increases the decision-makers’ workload, impacting their situational awareness and affecting the quality of soldiers’ decisions. This study uses a route clearance task as a use-case scenario to understand the issues in team-level decision-making in military tasks, the challenges in successfully completing the mission, and the demands placed on AI teammates when working in a human-AI team. We interviewed eight subject matter experts with prior experience in route clearance operations. The themes generated by analyzing the interview transcripts provide insights into soldiers’ perceptions of AI teammates as well as recommendations for successfully integrating human-AI teams in military settings.

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Section: Introductionmentioning

confidence: 99%