The forgotten teammate: Considering the labor perspective in human-autonomy teams

Begerowski, Sydney R.; Hedrick, Katelyn N.; Mears, Laine; Shuffler, Marissa L.

doi:10.1016/j.chb.2023.107763

Cited by 6 publications

(1 citation statement)

References 77 publications

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“…Ubiquitous and multiple-dynamic (Reason, 1990) interactions between two fundamentally different agents, humans as analog beings (Norman, 1998) and digital computers, each relying on imperfect and different but interactively and dynamically shaped models of each other, present additional research problems (Begerowski, Hedrick, Waldherr, Mears, & Shuffler, 2023;O'Neill, Flathmann, McNeese, & Salas, 2023;O'Neill et al, 2022;Stowers, Brady, MacLellan, Wohleber, & Salas, 2021). AI is trained by experience with human interactions, but these interactions are also influenced by the human experience with the AI agent.…”

Section: Introductionmentioning

confidence: 99%

Measuring Temporal Awareness for Human-Aware AI

Gray,

Yong,

Wasti

et al. 2023

Proceedings of the Human Factors and Ergonomics Society Annual

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This research investigated human performance in response to task demands that may be used to convey information about the human to an artificial agent. We performed an experiment with a dynamic time-sharing task to investigate participants development of temporal awareness of the task event unfolding in time. Temporal awareness as an extension, or a special case, of situation awareness, may provide for useful measures of covert mental models applicable to numerous tasks and for input to human-aware AI agents. Temporal awareness measures may be used to classify human performance into the control modes in the contextual control model (COCOM): scrambled, opportunistic, tactical, and strategic. Twenty-one participants participated in a withinsubjects experiment with an abstract task of resetting four independent timers within their respective windows of opportunity. The results show that temporal measures of task performance are sensitive to changes in task disruptions and difficulty and therefore have promise for human-aware AI.

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Section: Introductionmentioning

confidence: 99%

Measuring Temporal Awareness for Human-Aware AI

Gray,

Yong,

Wasti

et al. 2023

Proceedings of the Human Factors and Ergonomics Society Annual

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Stepping out of the shadow of human-human teaming: Crafting a unique identity for human-autonomy teams

McNeese

Flathmann

O’Neill

et al. 2023

Computers in Human Behavior

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To Share or Not to Share: Understanding and Modeling Individual Disclosure Preferences in Recommender Systems for the Workplace

Musick,

Duan,

Najafian

et al. 2024

Proc. ACM Hum.-Comput. Interact.

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Newly-formed teams often encounter the challenge of members coming together to collaborate on a project without prior knowledge of each other's working and communication styles. This lack of familiarity can lead to conflicts and misunderstandings, hindering effective teamwork. Derived from research in social recommender systems, team recommender systems have shown the ability to address this challenge by providing personality- derived recommendations that help individuals interact with teammates with differing personalities. However, such an approach raises privacy concerns as to whether teammates would be willing to disclose such personal information with their team. Using a vignette survey conducted via a research platform that hosts a team recommender system, this study found that context and individual differences significantly impact disclosure preferences related to team recommender systems. Specifically, when working in interdependent teams where success required collective performance, participants were more likely to disclose personality information related to Emotionality and Extraversion unconditionally. Drawing on these findings, this study created and evaluated a machine learning model to predict disclosure preferences based on group context and individual differences, which can help tailor privacy considerations in team recommender systems prior to interaction.

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The forgotten teammate: Considering the labor perspective in human-autonomy teams

Cited by 6 publications

References 77 publications

Measuring Temporal Awareness for Human-Aware AI

Measuring Temporal Awareness for Human-Aware AI

Stepping out of the shadow of human-human teaming: Crafting a unique identity for human-autonomy teams

To Share or Not to Share: Understanding and Modeling Individual Disclosure Preferences in Recommender Systems for the Workplace

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