Improving Teamwork Competencies in Human-Machine Teams: Perspectives From Team Science

Stowers, Kimberly; Brady, Lisa; MacLellan, Christopher J.; Wohleber, Ryan W.; Salas, Eduardo

doi:10.3389/fpsyg.2021.590290

Cited by 28 publications

(24 citation statements)

References 49 publications

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“…[ 10 ] The current AI research is focusing on imparting these transportable competencies to the machines and addressing their requirements to be effective teammates. [ 46 ]…”

Section: Discussionmentioning

confidence: 99%

The future of artificial intelligence in neurosurgery: A narrative review

Iqbal

Jahangir

Mashkoor

et al. 2022

Surgical Neurology International

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Background: Artificial intelligence (AI) and machine learning (ML) algorithms are on the tremendous rise for being incorporated into the field of neurosurgery. AI and ML algorithms are different from other technological advances as giving the capability for the computer to learn, reason, and problem-solving skills that a human inherits. This review summarizes the current use of AI in neurosurgery, the challenges that need to be addressed, and what the future holds. Methods: A literature review was carried out with a focus on the use of AI in the field of neurosurgery and its future implication in neurosurgical research. Results: The online literature on the use of AI in the field of neurosurgery shows the diversity of topics in terms of its current and future implications. The main areas that are being studied are diagnostic, outcomes, and treatment models. Conclusion: Wonders of AI in the field of medicine and neurosurgery hold true, yet there are a lot of challenges that need to be addressed before its implications can be seen in the field of neurosurgery from patient privacy, to access to high-quality data and overreliance on surgeons on AI. The future of AI in neurosurgery is pointed toward a patient-centric approach, managing clinical tasks, and helping in diagnosing and preoperative assessment of the patients.

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“…[ 10 ] The current AI research is focusing on imparting these transportable competencies to the machines and addressing their requirements to be effective teammates. [ 46 ]…”

Section: Discussionmentioning

confidence: 99%

The future of artificial intelligence in neurosurgery: A narrative review

Iqbal

Jahangir

Mashkoor

et al. 2022

Surgical Neurology International

View full text Add to dashboard Cite

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“…Such capabilities are essential in situations where the cost-of-failure is high [45]. We believe that PATRA is one of many team design patterns that potentially improve the coordination of human-machine teams [46], in which theory of mind plays an important role.…”

Section: Discussion On the Effects Of Explanationsmentioning

confidence: 99%

“I’m Afraid I Can’t Do That, Dave”; Getting to Know Your Buddies in a Human–Agent Team

Schadd¹,

Schoonderwoerd²,

Bosch³

et al. 2022

Systems

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The rapid progress in artificial intelligence enables technology to more and more become a partner of humans in a team, rather than being a tool. Even more than in human teams, partners of human–agent teams have different strengths and weaknesses, and they must acknowledge and utilize their respective capabilities. Coordinated team collaboration can be accomplished by smartly designing the interactions within human–agent teams. Such designs are called Team Design Patterns (TDPs). We investigated the effects of a specific TDP on proactive task reassignment. This TDP supports team members to dynamically allocate tasks by utilizing their knowledge about the task demands and about the capabilities of team members. In a pilot study, agent–agent teams were used to study the effectiveness of proactive task reassignment. Results showed that this TDP improves a team’s performance, provided that partners have accurate knowledge representations of each member’s skill level. The main study of this paper addresses the effects of task reassignments in a human–agent team. It was hypothesized that when agents provide explanations when issuing and responding to task reassignment requests, this will enhance the quality of the human’s mental model. Results confirmed that participants developed more accurate mental models when agent-partners provide explanations. This did not result in a higher performance of the human–agent team, however. The study contributes to our understanding of designing effective collaboration in human–agent teams.

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“…Communication, coordination, and adaptation are qualities that are critical to human-machine teaming, but machines have not yet fully realized the unique human cognition for effective teaming. The rapid technological advancements in the recent era, especially in the field of AI, have offered new capabilities to machines to maximize their competencies in this regard [ 21 ]. According to Seeber et al [ 22 ] rapid technological advancements have enabled machines to acquire transportable teamwork competencies that are critical to teams.…”

Section: Human-machine Teaming and Computer Vision In Cardiothoracic ...mentioning

confidence: 99%

“…Using this model, the machine can know what information is available to teammates and what would be the subsequent action of their human teammates, creating better coordination with the human counterparts in a team. This model will also be able to determine when and how best to communicate with teammates, further enhancing ability and trust in machines [ [21] , [22] , [23] , [24] ]. Current AI research focuses on specific objectives to be achieved by machines and doesn't incorporate many findings from teaming and human-computer interface literature.…”

Section: Human-machine Teaming and Computer Vision In Cardiothoracic ...mentioning

confidence: 99%