Human-autonomy Teaming: Need for a guiding team-based framework?

O’Neill, Thomas A.; Flathmann, Christopher; McNeese, Nathan J.; Salas, Eduardo

doi:10.1016/j.chb.2023.107762

Cited by 20 publications

(8 citation statements)

References 41 publications

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“…Our research is a starting point for further explorations of trust in HAITs and for identifying relevant factors and mechanisms that contribute to the development of trust towards AI teammates. Specifically, our evidence suggests that human-only team research can inform our understanding of HAITs (O'Neill et al, 2023), but further investigation is required to determine whether existing trust theories need expansion . Specifically, there is a need to gain a better understanding of the distinction between cognitive and affective interpersonal trust and to clarify whether and to what extent the two trust dimensions are needed for HAIT success.…”

Section: Implications For Theory Research and Practicementioning

confidence: 95%

My colleague is an AI! Trust differences between AI and human teammates

Georganta,

Ulfert

2024

TPM

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Purpose The purpose of this study was to investigate trust within human-AI teams. Trust is an essential mechanism for team success and effective human-AI collaboration. Design/methodology/approach In an online experiment, the authors investigated whether trust perceptions and behaviours are different when introducing a new AI teammate than when introducing a new human teammate. A between-subjects design was used. A total of 127 subjects were presented with a hypothetical team scenario and randomly assigned to one of two conditions: new AI or new human teammate. Findings As expected, perceived trustworthiness of the new team member and affective interpersonal trust were lower for an AI teammate than for a human teammate. No differences were found in cognitive interpersonal trust and trust behaviours. The findings suggest that humans can rationally trust an AI teammate when its competence and reliability are presumed, but the emotional aspect seems to be more difficult to develop. Originality/value This study contributes to human–AI teamwork research by connecting trust research in human-only teams with trust insights in human–AI collaborations through an integration of the existing literature on teamwork and on trust in intelligent technologies with the first empirical findings on trust towards AI teammates.

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Section: Implications For Theory Research and Practicementioning

confidence: 95%

My colleague is an AI! Trust differences between AI and human teammates

Georganta,

Ulfert

2024

TPM

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“…Thus, the foundational nature of teams lies in interdependence, where the individual's success is inextricably linked to the success of the collective (O'Neill et al, 2023;.…”

Section: Coordination In Dynamic Systemsmentioning

confidence: 99%

“…In sum, the paradigm of machines as fully-fledged teammates introduces an array of complexities that, thus far, can often not be matched with traditional approaches to all-human teams (Endsley, 2023a;O'Neill et al, 2023). The capabilities and dynamics in HATs can constrain the emergence of coordination and narrow the range of possible interactions and outcomes.…”

Section: The Distinct Nature Of Coordination In Hatsmentioning

confidence: 99%

“…However, in light of the distinctive nature of HATs, the emergence of coordination can become disintegrated, disrupted or rigid. This threatens HATs ability to realize their potential as they must become adaptive to perform in volatile industry environments such as aviation or healthcare, calling for a perspective that recognizes coordination challenges between humans and autonomous machines (O'Neill et al, 2023).…”

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confidence: 99%

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Supporting Adaptive Coordination in Human-Autonomy-Teams: A Conceptual Framework

Nols,

Ulfert,

Gevers

2024

Preprint

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“…To date, research has focused on individual facets of successful HAT, ignoring the Input-Process-Output (IPO) framework (Hackman, 1987 ) in teamwork research (cf. O'Neill et al, 2023 ) which acknowledges the pivotal role of group processes (e.g., shared mental models or communication) in converting inputs (e.g., autonomy or task) into desired outcomes (e.g., team performance or work satisfaction). Often, the focus is on single aspects such as trust (Lyons et al, 2022 ), agent autonomy (Ulfert et al, 2022 ), shared mental models (Andrews et al, 2023 ), or speech (Bogg et al, 2021 ).…”

Section: Human-ai Multi-team Systemsmentioning

confidence: 99%

Human-AI teams—Challenges for a team-centered AI at work

Hagemann,

Rieth,

Suresh

et al. 2023

Front. Artif. Intell.

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As part of the Special Issue topic “Human-Centered AI at Work: Common Ground in Theories and Methods,” we present a perspective article that looks at human-AI teamwork from a team-centered AI perspective, i. e., we highlight important design aspects that the technology needs to fulfill in order to be accepted by humans and to be fully utilized in the role of a team member in teamwork. Drawing from the model of an idealized teamwork process, we discuss the teamwork requirements for successful human-AI teaming in interdependent and complex work domains, including e.g., responsiveness, situation awareness, and flexible decision-making. We emphasize the need for team-centered AI that aligns goals, communication, and decision making with humans, and outline the requirements for such team-centered AI from a technical perspective, such as cognitive competence, reinforcement learning, and semantic communication. In doing so, we highlight the challenges and open questions associated with its implementation that need to be solved in order to enable effective human-AI teaming.

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Human-autonomy Teaming: Need for a guiding team-based framework?

Cited by 20 publications

References 41 publications

My colleague is an AI! Trust differences between AI and human teammates

My colleague is an AI! Trust differences between AI and human teammates

Supporting Adaptive Coordination in Human-Autonomy-Teams: A Conceptual Framework

Human-AI teams—Challenges for a team-centered AI at work

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