Human Team Research Articles

Human-AI teaming: leveraging transactive memory and speaking up for enhanced team effectiveness.

In this prospective observational study, we investigate the role of transactive memory and speaking up in human-AI teams comprising 180 intensive care (ICU) physicians and nurses working with AI in a simulated clinical environment. Our findings indicate that interactions with AI agents differ significantly from human interactions, as accessing information from AI agents is positively linked to a team's ability to generate novel hypotheses and demonstrate speaking-up behavior, but only in higher-performing teams. Conversely, accessing information from human team members is negatively associated with these aspects, regardless of team performance. This study is a valuable contribution to the expanding field of research on human-AI teams and team science in general, as it emphasizes the necessity of incorporating AI agents as knowledge sources in a team's transactive memory system, as well as highlighting their role as catalysts for speaking up. Practical implications include suggestions for the design of future AI systems and human-AI team training in healthcare and beyond.

Modeling Team Interaction and Decision-Making in Agile Human–Machine Teams: Quantum and Dynamical Systems Perspective

In this study, we define team agility as a function of exploration and exploitation of team coordination. Based on these two coordination concepts, we examined interactive decision-making in a dynamic task environment by applying: first, the principles of quantum cognition for the decision-making processes at the confluence of teamwork and taskwork (to discern the effects of ontic uncertainty for each human team member in the case of having incomplete teamwork) and second, nonlinear dynamical systems modeling for the teamwork (to capture epistemic uncertainty). In this study, there were the following three conditions based on manipulation of the pilot role: first, synthetic condition—the pilot role was played by a synthetic agent, second, control condition—it was a randomly assigned participant, and third, experimenter condition—it was an expert who used a role-specific coordination script. Overall findings indicate that when teams in the experimenter condition come across the targets, they tend to explore alternatives by coordinating as a team rather than exploiting existing team strategies, which may not work best for the situation at hand. In contrast, teams in control and synthetic conditions tended more toward exploitation than exploration in coordination. We consider this a sign of agility in teamwork.

What Patients Say: Large-Scale Analyses of Replies to the Parkinson's Disease Patient Report of Problems (PD-PROP).

Free-text, verbatim replies in the words of people with Parkinson's disease (PD) have the potential to provide unvarnished information about their feelings and experiences. Challenges of processing such data on a large scale are a barrier to analyzing verbatim data collection in large cohorts. To develop a method for curating responses from the Parkinson's Disease Patient Report of Problems (PD-PROP), open-ended questions that asks people with PD to report their most bothersome problems and associated functional consequences. Human curation, natural language processing, and machine learning were used to develop an algorithm to convert verbatim responses to classified symptoms. Nine curators including clinicians, people with PD, and a non-clinician PD expert classified a sample of responses as reporting each symptom or not. Responses to the PD-PROP were collected within the Fox Insight cohort study. Approximately 3,500 PD-PROP responses were curated by a human team. Subsequently, approximately 1,500 responses were used in the validation phase; median age of respondents was 67 years, 55% were men and median years since PD diagnosis was 3 years. 168,260 verbatim responses were classified by machine. Accuracy of machine classification was 95% on a held-out test set. 65 symptoms were grouped into 14 domains. The most frequently reported symptoms at first report were tremor (by 46% of respondents), gait and balance problems (>39%), and pain/discomfort (33%). A human-in-the-loop method of curation provides both accuracy and efficiency, permitting a clinically useful analysis of large datasets of verbatim reports about the problems that bother PD patients.

Animals Helping Animals: How Dog Detectives Can Help Save Endangered Species

Dogs are working alongside humans to help protect animals in the wild that are endangered. People who work in the field of animal conservation can train dogs to use their powerful noses to sniff out where certain endangered species have been. Dogs help their human handlers by finding the scat (poop!) that members of an endangered species have left behind. Scat contains important information about these species that help us preserve their populations. In this article, you will learn why some animals are endangered, why studying scat is so important, and how teams of dogs and humans can work together to find scat.

Comparison and synthesis of two aerospace case studies to develop human-autonomy teaming requirements

This paper developed human-autonomy teaming (HAT) characteristics and requirements by comparing and synthesizing two aerospace case studies (Single Pilot Operations/Reduced Crew Operations and Long-Distance Human Space Operations) and the related recent HAT empirical studies. Advances in sensors, machine learning, and machine reasoning have enabled increasingly autonomous system technology to work more closely with human(s), often with decreasing human direction. As increasingly autonomous systems become more capable, their interactions with humans may evolve into a teaming relationship. However, humans and autonomous systems have asymmetric teaming capabilities, which introduces challenges when designing a teaming interaction paradigm in HAT. Additionally, developing requirements for HAT can be challenging for future operations concepts, which are not yet well-defined. Two case studies conducted previously document analysis of past literature and interviews with subject matter experts to develop domain knowledge models and requirements for future operations. Prototype delegation interfaces were developed to perform summative evaluation studies for the case studies. In this paper, a review of recent literature on HAT empirical studies was conducted to augment the document analysis for the case studies. The results of the two case studies and the literature review were compared and synthesized to suggest the common characteristics and requirements for HAT in future aerospace operations. The requirements and characteristics were grouped into categories of team roles, autonomous teammate types, interaction paradigms, and training. For example, human teammates preferred the autonomous teammate to have human-like characteristics (e.g., dialog-based conversation, social skills, and body gestures to provide cue-based information). Even though more work is necessary to verify and validate the requirements for HAT development, the case studies and recent empirical literature enumerate the types of functions and capabilities needed for increasingly autonomous systems to act as a teammate to support future operations.

Sensitivity Analysis for Dynamic Control of PSTNs with Skewed Distributions

Probabilistic Simple Temporal Networks (PSTN) facilitate solving many interesting scheduling problems by characterizing uncertain task durations with unbounded probabilistic distributions. However, most current approaches assess PSTN performance using normal or uniform distributions of temporal uncertainty. This paper explores how well such approaches extend to families of non-symmetric distributions shown to better represent the temporal uncertainty introduced by, e.g., human teammates by building new PSTN benchmarks. We also build probability-aware variations of current approaches that are more reactive to the shape of the underlying distributions. We empirically evaluate the original and modified approaches over well-established PSTN datasets. Our results demonstrate that alignment between the planning model and reality significantly impacts performance. While our ideas for augmenting existing algorithms to better account for human-style uncertainty yield only marginal gains, our results surprisingly demonstrate that existing methods handle positively-skewed temporal uncertainty better.

Revisiting ESKAPE Pathogens: virulence, resistance, and combating strategies focusing on quorum sensing.

The human-bacterial association is long-known and well-established in terms of both augmentations of human health and attenuation. However, the growing incidents of nosocomial infections caused by the ESKAPE pathogens (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter sp.) call for a much deeper understanding of these organisms. Adopting a holistic approach that includes the science of infection and the recent advancements in preventing and treating infections is imperative in designing novel intervention strategies against ESKAPE pathogens. In this regard, this review captures the ingenious strategies commissioned by these master players, which are teamed up against the defenses of the human team, that are equally, if not more, versatile and potent through an analogy. We have taken a basketball match as our analogy, dividing the human and bacterial species into two teams playing with the ball of health. Through this analogy, we make the concept of infectious biology more accessible.

Embodied, Intelligent Communication for Multi-Agent Cooperation

High-performing human teams leverage intelligent and efficient communication and coordination strategies to collaboratively maximize their joint utility. Inspired by teaming behaviors among humans, I seek to develop computational methods for synthesizing intelligent communication and coordination strategies for collaborative multi-robot systems. I leverage both classical model-based control and planning approaches as well as data-driven methods such as Multi-Agent Reinforcement Learning (MARL) to provide several contributions towards enabling emergent cooperative teaming behavior across both homogeneous and heterogeneous (including agents with different capabilities) robot teams.

CONCEPTUAL FRAMEWORK TO STUDY TEAM COHESION IN HUMAN-ROBOT TEAMS

Abstractuse-case scenarios, including homes, hospitals, workplaces, and recreation. Though the area of Social Robotics has gained traction in recent years, the majority of the studies so far have studied single-human and single-robot interaction. In comparison, Social Robots are increasingly being placed in human teams, likely affecting team dynamics. On the other hand, Engineering teams work together to deliver outstanding results and the processes in these teams are social. We propose that Social robot can be added to engineering human team to enhance team cohesion and performance. Therefore, this paper presents a preliminary framework towards developing a conceptual framework to study team cohesion in Human-Robot Teams (HRTs) in engineering context, looks at different roles of social robot and how the responses, behaviours, emotions of social robots shape outcomes in the engineering team. The research specifically focuses on team cohesion because team cohesion is reportedly one of the most critical concepts in team dynamics. The paper outlines the research objectives, framework and concept workflow.

FOCUS AND MODALITY: DEFINING A ROADMAP TO FUTURE AI-HUMAN TEAMING IN DESIGN

AbstractThe evolution of Artificial Intelligence (AI) and Machine Learning (ML) enables new ways to envision how computer tools will aid, work with, and even guide human teams. This paper explores this new paradigm of design by considering emerging variations of AI-Human collaboration: AI used as a design tool versus AI employed as a guide to human problem solvers, and AI agents which only react to their human counterparts versus AI agents which proactively identify and address needs. The different combinations can be mapped onto a 2×2 AI-Human Teaming Matrix which isolates and highlights these different AI capabilities in teaming. The paper introduces the matrix and its quadrants, illustrating these different AI agents and their application and impact, and then provides a road map to researching and developing effective AI team collaborators.

Charismatic speech features in robot instructions enhance team creativity

This study examines whether a social robot can enable team creativity and increase team performance depending on its speaking style. The aim is to provide insight into human teams' creativity and performance when exposed to different ways of speaking by a social robot, that is, when the robotic creativity facilitator is using different acoustic-prosodic features. In one condition, participants received their instructions from the robot in a “charismatic” speaking style, in the other, the robot used a less engaging way of speaking. The results show that when the robot's speech is based on charismatic speech characteristics, it is significantly better at enhancing team creativity and performance than when its speech uses fewer charismatic speech characteristics. The robot's speaking style thus influences its effectiveness as team creativity facilitator.

Relative Effects of Positive and Negative Explanations on Satisfaction and Performance in Human-Agent Teams

Improving agent capabilities and increasing availability of computing platforms and Internet connectivity allows for more effective and diverse collaboration between human users and automated agents. To increase the viability and effectiveness of human-agent collaborative teams, there is a pressing need for research enabling such teams to maximally leverage relative strengths of human and automated reasoners. We study virtual and ad-hoc teams, comprising a human and an agent, collaborating over a few episodes where each episode requires them to complete a set of tasks chosen from given task types. Team members are initially unaware of the capabilities of their partners, and the agent, acting as the task allocator, has to adapt the allocation process to maximize team performance. The focus of the current paper is on analyzing how allocation decision explanations can affect both user performance and the human workers' outlook including factors such as motivation and satisfaction. We investigate the effect of explanations provided by the agent allocator to the human on performance and key factors reported by the human teammate on surveys. Survey factors include the effect of explanations on motivation, explanatory power, and understandability, as well as satisfaction with and trust / confidence in the teammate. We evaluated a set of hypotheses on these factors related to positive, negative, and no-explanation scenarios through experiments conducted with MTurk workers.

Choosing the Task Allocator: Effect on Performance and Satisfaction in Human-Agent Team

Ad hoc human-agent teams, where team members interact without prior experience with teammates and only for a limited number of interactions, will be commonplace in dynamic environments with opportunity windows for collaboration between diverse groups. We study the efficacy of virtual ad-hoc teams, consisting of a human and an agent, collaborating to complete tasks in each of a few episodes. To maximize team potential, the relative expertise of team members must be measured and utilized in allocating tasks. As team members are not initially aware of each other's task competence and as humans often cannot accurately estimate their competencies, adapting allocation over the episodes is critical to team performance. Human team member satisfaction with allocations is also critical to determining team viability. We therefore use both these criteria to measure the effectiveness of task allocation procedures with varying degree of flexibility and human teammate control: (a) alternating, (b) performance adaptive, (c) agent-guided, (d) human-selected. We report on the relative strengths of these allocation procedures based on results from experiments with MTurk workers.

Examining the impact of varying levels of AI teammate influence on human-AI teams

The implementation of AI teammates is creating a wealth of research that examines how AI teammates impact human-AI teams. However, AI teammates themselves are not static, and their roles and responsibilities in human-AI teams are likely to change as technologies advance in the coming years. As a result of this advancement, AI teammates will gain influence in teams, which refers to their ability to change and manipulate a team’s shared resources. This study uses a mixed-methods experiment to examine how the amount of influence AI teammates have on a team’s shared resources can impact the team outcomes of human teammate performance, teammate perceptions, and whole-team perception. Results indicate that AI teammates that increase their influence on shared resources over time can stagnate the improvement of human performance, but AI teammates that decrease their influence on shared resources can actually encourage humans to improve their own performance. Additionally, AI teammates that are highly influential on shared resources can make humans perceive a greater cognitive workload. However, qualitative results indicate that these impacts on human performance and perception do not consistently impact the acceptance humans form for AI teammates. Rather, humans form acceptance for AI teammates if said AI use its influence to manipulate resources to benefit the personal goals of human teammates. These results have critical implications for human-AI teaming as it shows that the influence AI teammates have on shared resources can be designed in a way that improves human performance. However, future research is going to need to focus more critically on how the personal goals humans have, which may not align with a team’s overall goals, are going to mediate the effectiveness of the AI teammate influence.

Shaping a multidisciplinary understanding of team trust in human-AI teams: a theoretical framework

ABSTRACT Intelligent systems are increasingly entering the workplace, gradually moving away from technologies supporting work processes to artificially intelligent (AI) agents becoming team members. Therefore, a deep understanding of effective human-AI collaboration within the team context is required. Both psychology and computer science literature emphasize the importance of trust when humans interact either with human team members or AI agents. However, empirical work and theoretical models that combine these research fields and define team trust in human-AI teams are scarce. Furthermore, they often lack to integrate central aspects, such as the multilevel nature of team trust and the role of AI agents as team members. Building on an integration of current literature on trust in human-AI teaming across different research fields, we propose a multidisciplinary framework of team trust in human-AI teams. The framework highlights different trust relationships that exist within human-AI teams and acknowledges the multilevel nature of team trust. We discuss the framework’s potential for human-AI teaming research and for the design and implementation of trustworthy AI team members.

Capturing the Dynamics of Trust and Team Processes in Human-Human-Agent Teams via Multidimensional Neural Recurrence Analyses

As collaborative technologies evolve from supportive tools to interactive teammates, there is a growing need to understand how trust and team processes develop in human-agent teams. To contribute effectively, these systems must be able to support human teammates in a task without disrupting the delicate interpersonal states and team processes that govern successful collaboration. In order to break down the complexity of monitoring multiple actors in human-agent collaborations, there is a need to identify interpretable, generalizable measures that can monitor the emergence of interpersonal and team-level processes that underlie effective teaming. We address this gap by using functional Near-Infrared Spectroscopy to concurrently measure brain activity of two individuals in a human-human-agent team during a complex, ecologically valid collaborative task, with a goal of identifying quantitative markers of cognition- and affect-based trust alongside team processes of coordination, strategy formulation, and affect management. Two multidimensional extensions of recurrence quantification analysis, a nonlinear method based in dynamical systems theory, are presented to quantify interpersonal coupling and team-level regularity as reflected in the hemodynamics of three cortical regions across multiple time-scales. Mixed-effects regressions reveal that neural recurrence between individuals uniquely reflects changes in self-reported trust, while team-level neural regularity inversely predicts self-reported team processes. Additionally, we show that recurrence metrics capture temporal dynamics of affect-based trust consistent with existing theory, showcasing the interpretability and specificity of these metrics for disentangling complex team states and processes. This paper presents a novel, interpretable, and computationally efficient model-free method capable of differentiating between latent trust and team processes a complex, naturalistic task setting. We discuss the potential applications of this technique for continuous monitoring of team states, providing clear targets for the future development of adaptive human-agent teaming systems.

AI Agents as Team Members: Effects on Satisfaction, Conflict, Trustworthiness, and Willingness to Work With

ABSTRACT Organizations are beginning to deploy artificial intelligence (AI) agents as members of virtual teams to help manage information, coordinate team processes, and perform simple tasks. How will team members perceive these AI team members and will they be willing to work with them? We conducted a 2 x 2 x 2 lab experiment that manipulated the type of team member (human or AI), their performance (high or low), and the performance of other team members (high or low). AI team members were perceived to have higher ability and integrity but lower benevolence, which led to no differences in trustworthiness or willingness to work with them. However, the presence of an AI team member resulted in lower process satisfaction. When the AI team member performed well, participants perceived less conflict compared to a human team member with the same performance, but there were no differences in perceived conflict when it performed poorly. There were no other interactions with performance, indicating that the AI team member was judged similarly to humans, irrespective of variations in performance; there was no evidence of algorithm aversion. Our research suggests that AI team members are likely to be accepted into teams, meaning that many old collaboration research questions may need to be reexamined to consider AI team members.

Social perception in Human-AI teams: Warmth and competence predict receptivity to AI teammates

Advances in artificial intelligence (AI) promise a future where teams consist of people and intelligent machines, such as robots or virtual agents. In order for human-AI teams (HATs) to succeed, human team members will need to be receptive to their new AI counterparts. In this study, we draw on a tripartite model of human newcomer receptivity, which includes three components: reflection, knowledge utilization, and psychological acceptance. We hypothesize that two aspects of social perception—warmth and competence—are critical predictors of human receptivity to a new AI teammate. Study 1 uses a video vignette design in which participants imagine adding one of eight AI teammates to a referent team. Study 2 leverages a Wizard of Oz methodology in laboratory teams. In addition to testing the effects of perceived warmth and competence on receptivity components, Study 2 also explores the influence of receptivity components on perceived HAT viability. Though both studies find that perceived warmth and competence affect receptivity, we find competence is particularly important for knowledge utilization and psychological acceptance. Further, results of Study 2 show that psychological acceptance is positively related to perceived HAT viability. Implications for future research on social perception of AI teammates are discussed.

Understanding the impact and design of AI teammate etiquette

ABSTRACT Technical and practical advancements in Artificial Intelligence (AI) have led to AI teammates working alongside humans in an area known as human-agent teaming. While critical past research has shown the benefit to trust driven by the incorporation of interaction rules and structures (i.e. etiquette) in both AI tools and robotic teammates, research has yet to explicitly examine etiquette for digital AI teammates. Given the historic importance of trust within human-agent teams, the identification of etiquette’s impact within said teams should be paramount. Thus, this study empirically evaluates the impact of AI teammate etiquette through a mixed-methods study that compares AI teammates that either adhere to or ignore traditional etiquette standards for machine systems. The quantitative results show that traditional etiquette adherence leads to greater trust, perceived performance of the AI, and perceived performance of the team as a whole. However, qualitative results reveal that not all traditional etiquette behaviors have universal appeal due to the presence of individual differences. This research provides the first empirical and explicit exploration of etiquette within human-agent teams, and the results of this study should be used further design specific etiquette behaviors for AI teammates.

Minotaurs, Not Centaurs: The Future of Manned-Unmanned Teaming

Contesting Paul Scharre’s influential vision of “centaur warfighting” and the idea that autonomous weapon systems will replace human warfighters, this article proposes that the manned-unmanned teams of the future are more likely to be minotaurs, teams of humans under the control, supervision, or command of artificial intelligence. It examines the likely composition of the future force and prompts a necessary conversation about the ethical issues raised by minotaur warfighting.