Human Team Research Articles

Understanding the Evolvement of Trust Over Time within Human-AI Teams

The success of human-AI teams (HATs) requires humans to work with AI teammates in trustful ways over a certain time period. However, how trust evolves and changes dynamically in response to human-AI team interactions is generally understudied. This work explores the evolvement of trust in HATs over time by analyzing 45 participants' experiences of trust or distrust in an AI teammate prior to, during, and after collaborating with AI in a three-member HAT. Our findings highlight that humans' expectations of AI's ability, integrity, benevolence, and adaptability influence their initial trust in AI before collaboration. However, this initial trust can be maintained or revised through the development of situational trust during collaboration in response to the AI teammate's communication behaviors. Further, the trust developed through collaboration can impact individuals' subsequent expectations of AI's ability and their collaborations with AI. Our findings also reveal the similarities and differences in the temporal dimensions of trust for AI and human teammates. We contribute to CSCW community by offering one of the first empirical investigations into the dynamic and temporal dimension of trust evolvement in HATs. Our work yields insights into the pathways to expanding the methodological toolkit for investigating the development of trust in HATs, formulating theories of trust for the HAT context. These insights further inform the effective design of AI teammates and provide guidance on the timing, content, and methods for calibrating trust in future human-AI collaboration contexts.

How voice and helpfulness shape perceptions in human-agent teams

Voice assistants are increasingly prevalent, from personal devices to team environments. This study explores how voice type and contribution quality influence human-agent team performance and perceptions of anthropomorphism, animacy, intelligence, and trustworthiness. By manipulating both, we reveal mechanisms of perception and clarify ambiguity in previous work. Our results show that the human resemblance of a voice assistant’s voice negatively interacts with the helpfulness of an agent’s contribution to flip its effect on perceived anthropomorphism and perceived animacy. This means human teammates interpret the agent’s contributions differently depending on its voice. Our study found no significant effect of voice on perceived intelligence, trustworthiness, or team performance. We find differences in these measures are caused by manipulating the helpfulness of an agent. These findings suggest that function matters more than form when designing agents for high-performing human-agent teams, but controlling perceptions of anthropomorphism and animacy can be unpredictable even with high human resemblance.

UROLATAM Project: A multidisciplinary comprehensive care project for prostate cancer

Abstract Introduction Prostate Cancer (PCa) represents a health problem in the world and its management is heterogeneous in care, depending on the resources each country has, modifying its effectiveness in prevention, diagnosis, treatment and follow-up. Methods The UROLATAM program was implemented based on the Design Thinking quadruple helix innovation model, consisting of the immersion, design, idea and prototype phases, characterized by being multidisciplinary and comprehensive. Teachers and students from different careers at the Universidad Veracruzana participated. Linkages were made with: Local government, government health institutions, colleges of specialist doctors, private institutions and civil society. This program was broadcast on radio, television and social networks inviting the population to participate in education and prevention activities, in addition to participating in diagnosis, treatment and follow-up actions for men over 40 years of age. The duration of the program was 4 days, including educational sessions for doctors, patients, family members and caregivers, as well as determination of prostate-specific antigen, urological consultation and prostate ultrasound, as well as rehabilitation exercises, diagnostic confirmation and treatment. who needed it. Results During the sessions, 944 patients aged 60.7 (±15.0) years were treated, 443 (47%) had never received a urological consultation, identifying a prevalence of 11.7% of Cap. The human team present with the patient was 25 specialist doctors, 16 medical residents, 38 undergraduate students, 3 rehabilitators and 6 administrative staff. None of the participants received a salary for participating in this project. Conclusions The multidisciplinary UROLATAM linkage project constitutes a useful educational, training and assistance tool for the population, as well as an educational element for the students who are part of the work team. Key messages • The UROLATAM project is replicable in countries that require additional support in the prostate cancer approach. • The structure of the UROLATAM project can be replicated to address other public health problems.

Development of an Analysis of Alternatives Tool for Human-Agent Teaming Research

As human-agent teaming (HAT) research continues to proliferate, it becomes more difficult for both HAT researchers and agent developers to stay abreast of the literature. To this end, we developed an analysis of alternatives tool that enables The Contextual Labeling of Analytical Information into Relevant Valuable Options to Yield Alternatives for Novel Techniques (CLAIRVOYANT). Powered by a multiple criteria decision analysis (MCDA) framework, CLAIRVOYANT aids in hypothesis generation and experimental design by displaying summarized relationships observed in the HAT literature based on user needs. After iteratively designing literature-informed frameworks, generating an initial literature database, and instantiating the MCDA, a validation study was conducted demonstrating CLAIRVOYANT’s potential. Current limitations and future directions are discussed.

An Exploratory Study of Contextual Control Modes in Teamwork.

This study investigated the relationship between human team member contextual control and team performance under time constraints. Contextual control modes, which describe different strategies for action selection in dynamic environments, characterize how humans maintain performance under variable demands. Control modes have not yet been studied in teamwork settings. Modeling of the cross-level interaction between team members' control modes and emerging team behaviors can improve understanding of effective teamwork in dynamic environments. A human-subjects study explored the relationship between individual contextual control and team performance. Questionnaires about contextual control were used to elicit individual control modes. Analysis compared team members' control modes and investigated how control modes changed under varying time pressures. Participant's control modes differed in their look ahead horizon, the extensiveness of prior action evaluation, and their prior experience. Many team members shifted control modes during trials, resulting in both convergence and divergence of paired control modes. No effects on communication rate were found due to changes in team members' control modes, but partially significant findings may suggest that the control mode divergence affects performance. Teams can operate in multiple control mode configurations that change dynamically according to context. Further research with an increased sample size is warranted to analyze how time constraints influence team members' control modes and overall teaming processes and whether divergence of team control mode is favorable under time pressures. Further study of contextual control in teams may help improve team design to better support teams in coping with time constraints in dynamic environments.

Etyczne projektowanie w dobie generatywnej sztucznej inteligencji

Implementation of AI solutions is unavoidable on the markets, where the combination of time deficits and the need for constant creation of new solutions is especially demanding, that is i.a. in creative industries. Like a lens, generative AI concentrates the majority of threats and possibilities, resulting from technological changes of the recent decade. Still, despite the constant media confusion, great number of viral content, guides, courses and codes, we have much work to do towards defining the rules and specifics of designers’ collaboration with AI, and the related ethical challenges. The AI development is ahead of legal regulations and stimulates changes, and the constant information overload does not encourage reflection on AI systems and their role in design. Becoming aware of the possibilities and limitations of generative AI, however, is the only way to its sustainable and ethical use. The first step will be to reflect on and understand the possibilities and changes AI carries in the area of creation, intellectual property, and narrowing the competency gap (the lack of competency in reference to a given human team). The second step will be the efficient identification of risks and indication of new areas of responsibility to designers, who would like to apply new technologies in their work. Keywords: design, ethics, artificial intelligence (AI), responsibility, designer’s role

How can Artificial Intelligence Teammates Know What Humans Want? Using Eye-Tracking Data to Infer Human Preferences in Game-Theoretic Decision Tasks

For human-agent teams, it is as important for agents to have models of their human teammates as it is for humans to have models of their agent teammates. However, approaches to knowledge elicitation have wrestled with the problem of capturing human knowledge when much of it is tacit and difficult to verbalize. By observing the choices that people make under different conditions we can infer the choice structure they appear to be following. In this way, observations could allow the agent to predict its human team-mates’ choices and actions. We report an experiment in which eye-tracking is used to capture choices in a constrained task and then develop a decision model that can replicate aspects of these choices. We propose that such a model can support human-agent teams by enabling inferences of some aspects of human decision-making.

Human-Autonomy Teaming in a Cooperative Gamified Testbed: How Can AI Teammates Support Teamwork Processes?

When Artificial Intelligence (AI) agents are incorporated into teams, they are expected to possess teaming capabilities beyond their advanced technological skills. Human-Autonomy Teams (HATs) are AI agents and humans working interdependently toward shared goals. Teams engage in processes, including goal specification, planning, mission analysis, coordination, monitoring, and backup behaviors. Participation of AI agents in teamwork processes can elevate HAT effectiveness and enhance the involvement of the agents as teammates. This study explored three levels of support for teamwork processes from AI agents: passive, reactive, and proactive. The study was conducted in HiveMind, a cooperative gamified testbed. The proactive agent scored higher on several assessment metrics, including effective communication, interdependence, and teaming. Furthermore, participants reported positive perceptions of the agent’s proactive support and initiative taking. The study’s findings suggest that AI teammates’ level of participation in teamwork processes influences human teammate’s perceptions and teaming experiences with their AI teammate.

The group mind of hybrid teams with humans and intelligent agents in knowledge-intense work

Studies regularly demonstrate how well intelligent agents (IAs) can support humans or are demonstrably superior to them in some areas. Given that some tasks likely remain unsuitable for even the most intelligent machines in the mid-future, work in hybrid teams of humans and IAs—where the capabilities of both are effectively combined—will most likely shape the way we work in the coming decades. In an abductive study, we investigate an early example of hybrid teams, consisting of a conversational intelligent agent (IA) and humans, that aims to improve health behavior or change personality traits. We theorize Transactive Intelligent Memory System (TIMS) as a new vision of collaboration between humans and IAs in hybrid teams, based on our empirical insights and our literature review on transactive memory systems theory. Our empirical evidence shows that IAs can develop a form of individual and external memory, and hybrid teams of humans and IAs can realize joint systems of transactive memory—a competence that current literature only ascribes to humans. We further find that whether individuals view IAs merely as external memory aids or as part of their teams’ transactive memory is moderated by the tasks’ complexity and knowledge intensity, as well as the IA's ability to complete the task. This theorizing helps to better understand the role of IAs in future team-based working processes. Developers of IAs can use TIMS as a tool for requirements formulation to prepare their software agents for collaboration in hybrid teams.

Integrating artificial intelligence into team decision‐making: Toward a theory of AI–human team effectiveness

AbstractArtificial intelligence (AI), a technological advancement radically affecting the workplace of the future, offers benefits for decision‐making processes. The most critical organizational decisions typically occur through teams. However, team theories are fundamentally psychosocial, and AI disrupts this context by introducing a non‐human actor. Therefore, negative implications of AI's role must also be understood. Toward theory on the effective integration of AI into team decision‐making, we synthesize the literatures on team effectiveness and team decision‐making with research from cognate disciplines on human–technology interaction and teaming. Based on this synthesis, we offer propositions highlighting key variable relationships and negative side effects that must be accounted for in AI–human team decision‐making and follow with practical suggestions for management's adaption to this new context. Overall, our analysis emphasizes critical themes, constructs, and relationships valuable for further research aimed at modernizing theory and practice in the face of this emerging technological shift.

Human-AI Collaboration in Cooperative Games: A Study of Playing Codenames with an LLM Assistant

Playing partial information, restricted communication, cooperative (PIRCC) games with humans have proven challenging for AI, due to our reliance on social dynamics and sophisticated cognitive techniques. Yet, recent advances in generative AI may change this situation through new forms of human-AI collaboration. This paper investigates how teams of players interact with an AI assistant in the PIRCC game Codenames and the impact this has on cognition, social dynamics, and player experience. We observed gameplay and conducted post-game focus groups with 54 participants across ten groups. Each group played three rounds of Codenames, with an AI assistant supporting Cluegivers. We found the AI assistant enhanced players' convergent and divergent thinking, but interfered with formation of team mental models, highlighting a tension in the use of AI in creative team scenarios. The presence of the AI challenged many players' understanding of the 'spirit of the game'. Furthermore, the presence of the AI assistants weakened social connections between human teammates, but strengthened connections across teams. This paper provides an empirical account of an AI assistant's effect on cognition, social dynamics, and player experience in Codenames. We highlight the opportunities and challenges that arise when designing hybrid digital boardgames that include AI assistants.

No Laughing Matter: How Autonomous Agents Can Improve Well-Being and Teamwork

Autonomous agents are a critical factor for maintaining the well-being and health of astronaut teams in long duration spaceflight. As autonomous agents become more independent and interdependent the need to understand how they can interact effectively with humans not only becomes more necessary but will become the paradigm for future team research. In our paper, we describe a conceptual model that explains how autonomous agents and human team members can learn to implicitly coordinate through humorous social interaction and effective task interdependence. We argue that to build a shared mental model between autonomous agents and human team members, HATs need to develop trust with one another, albeit through different mechanisms, to work together effectively.

Moral behaviour alters impressions of humans and AIs on teams: unethical AIs are more powerful while ethical humans are nicer

ABSTRACT Artificial intelligence (AI) agents are increasingly being used as teammates, not just as tools, across many domains, and teammates’ moral behaviour can alter impressions of themselves and the team. How good, powerful, and active is an AI versus human team member engaging in an ethical or unethical behaviour? How good, powerful, and active is their team? To address these questions, we conduct four studies across three domains (chess, esports, and poetry composition) where participants rate their impressions of team members and teams presented in a scenario. In the scenario, a member of a hybrid team of 2 humans and 2 AIs is presented with an opportunity to cheat, and either does or does not. We manipulate which team member (AI vs human) is acting and the morality of that action (non-cheating vs cheating). Across the studies, results show that ethical behaviour significantly increases the goodness of the human more than the AI, and unethical behaviour significantly increases the power of the AI more than the human. However, there were no systematic human versus AI differences on team impressions.

Advancements in artificial intelligence for omnichannel marketing and customer service: Enhancing predictive analytics, automation, and operational efficiency

Gone are the days of impersonal customer service and generic marketing. Artificial intelligence (AI) is changing how businesses interact with customers, making interactions personalized and smooth across every platform. This transformation is powered by AI's capability to predict what customers want, automate routine tasks, and analyze large volumes of data. The Predictive Power of AI examines customer data to predict their preferences. Imagine receiving targeted ads and product recommendations that seem tailored exactly to your preferences! This personalized marketing approach enhances engagement and boosts sales significantly. AI-powered 24/7 chatbots provide instant customer support, eliminating the need for waiting on hold. They handle common inquiries and offer real-time assistance, continuously improving their ability to understand natural language and provide increasingly accurate help with each interaction. AI provides efficiency boost by automating repetitive tasks, allowing human teams to dedicate more time to cultivating relationships with customers, this includes qualifying leads, building targeted email campaigns, and analyzing data to inform business decisions, the outcome? More efficient operations and a happier team. It is imperative for businesses to understand customer sentiments and integration of AI analyzes customer interactions across various channels to assess their emotions and attitudes. This process, known as "sentiment analysis," enables businesses to detect dissatisfaction early, manage online brand perception, and gather valuable insights to enhance products and refine marketing strategies. There are also concerns about Ethical considerations, as AI becomes more integrated, businesses must prioritize customer privacy, eliminate bias in algorithms, and maintain a human touch to build genuine relationships. The future of AI and its possibilities are limitless! Natural Language Processing (NLP) advancements will lead to chatbots that sound and act human, while the integration of AI with virtual reality (VR) and augmented reality (AR) could create immersive and personalized experiences. In summary, AI is not here to replace humans; it is here to collaborate as your customer experience partner.

Modeling perceived information needs in human-AI teams: improving AI teammate utility and driving team cognition

ABSTRACT As AI technologies advance, teams are beginning to see AI transition from a tool to a full-fledged teammate. Introducing an AI teammate brings several challenges, ranging from how human teammates perceive their new AI teammates from an affective standpoint to how AI should engage in the various teaming behaviors that make up effective teamwork. The current study used a mixed factorial survey and structural equation modeling to assess how participants in hypothetical human-AI teams respond to various forms of AI information-sharing, including information related to explainability, back-up behavior, situational awareness, and augmenting team memory. The study's results found that AI design features related to situational awareness and augmenting the teams' memory had the strongest effect on participants' attitudes and perceived team cognition with their teammates. However, much of this effect was mediated by participants' affective attitudes towards the AI as a teammate, with higher ratings leading directly to higher levels of perceived team cognition constructs. These results highlight the importance of fostering positive attitudes towards AI teammates, such as trust and cohesion in human-AI teams, to support the development of effective team cognition and the ability of AI information-sharing to bring about such positive impacts.

What you say vs what you do: Utilizing positive emotional expressions to relay AI teammate intent within human–AI teams

With the expansive growth of AI’s capabilities in recent years, researchers have been tasked with developing and improving human-centered AI collaborations, necessitating the creation of human–AI teams (HATs). However, the differences in communication styles between humans and AI often prevent human teammates from fully understanding the intent and needs of AI teammates. One core difference is that humans naturally leverage a positive emotional tone during communication to convey their confidence or lack thereof to convey doubt in their ability to complete a task. Yet, this communication strategy must be explicitly designed in order for an AI teammate to be human-centered. In this mixed-methods study, 45 participants completed a study examining how human teammates interpret the behaviors of their AI teammates when they express different positive emotions via specific words/phrases. Quantitative results show that, based on corresponding behaviors, AI teammates were able to use displays of emotion to increase trust in AI teammates and the positive mood of the human teammate. Additionally, our qualitative findings indicate that participants preferred their AI teammates to increase the intensity of their displayed emotions to help reduce the perceived risk of their AI teammate’s behavior. When taken in sum, these findings describe the relevance of AI teammates expressing intensities of emotion while performing various behavioral decisions as a continued means to provide social support to the wider team and better task performance.

Exploring Connections Between Team Process and Automation Error Management

In complex social-technical systems, human teams are required to handle automation errors to maintain system performance. This study developed two path models that connected the team process variables with the automation error management process, based on data collected from a previous experiment. The models show that trust has negative effects on most of the management activities. Team situation awareness could facilitate the whole process by supporting information selecting activity during the analysis and planning stage. Better teamwork could assist the stages of error detection and analysis and planning. Teams with more balanced decision making and workload would find it easier to understand and handle an automation error. The results also reveal that forming an interpretation of the error is helpful but not a necessity for error management. Path models developed in this study provide a technical basis for better team processes in the face of automation error.

Human factors considerations for the context-aware design of adaptive autonomous teammates

Despite the gains in performance that AI can bring to human-AI teams, they also present them with new challenges, such as the decline in human ability to respond to AI failures as the AI becomes more autonomous. This challenge is particularly dangerous in human-AI teams, where the AI holds a unique role in the team’s success. Thus, it is imperative that researchers find solutions for designing AI team-mates that consider their human team-mates’ needs in their adaptation logic. This study explores adaptive autonomy as a solution to overcoming these challenges. We conducted twelve contextual inquiries with professionals in two teaming contexts in order to understand how human teammate perceptions can be used to determine optimal autonomy levels for AI team-mates. The results of this study will enable the human factors community to develop AI team-mates that can enhance their team’s performance while avoiding the potentially devastating impacts of their failures.

Introduction to the Emerging Cognitive Science of Distributed Human-Autonomy Teams.

Teams are a fundamental aspect of life-from sports to business, to defense, to science, to education. While the cognitive sciences tend to focus on information processing within individuals, others have argued that teams are also capable of demonstrating cognitive capacities similar to humans, such as skill acquisition and forgetting (cf., Cooke, Gorman, Myers, & Duran, 2013; Fiore etal., 2010). As artificially intelligent and autonomous systems improve in their ability to learn, reason, interact, and coordinate with human teammates combined with the observation that teams can express cognitive capacities typically seen in individuals, a cognitive science of teams is emerging. Consequently, new questions are being asked about teams regarding teamness, trust, the introduction and effects of autonomous systems on teams, and how best to measure team behavior and phenomena. In this topic, four facets of human-autonomy team cognition are introduced with leaders in the field providing in-depth articles associated with one or more of the facets: (1) defining teams; (2) how trust is established, maintained, and repaired when broken; (3) autonomous systems operating as teammates; and (4) metrics for evaluating team cognition across communication, coordination, and performance.

Partitioned scheduling with safety-performance trade-offs in stochastic conditional DAG models

This paper is motivated by robotic systems that solve difficult real-world problems such as search and rescue (SAR) or precision agriculture 11This work was supported by NSF under Grant CNS-1932074.. These applications require robots to operate in complex, uncertain environments while maintaining safe interactions with human teammates within a specified level of performance. In this paper, we study the scheduling of real-time applications on heterogeneous hardware platforms inspired by such contexts. To capture the stochasticity due to unpredictable environments, we propose the stochastic heterogeneous parallel conditional DAG (SHPC-DAG) model, which extends the most recent HPC-DAG model in two regards. First, it uses conditional DAG nodes to model the execution of computational pipelines based on context, while the stochasticity of DAG edges captures the uncertain nature of a system’s environment or the reliability of its hardware. Second, considering the pessimism of deterministic worst-case execution time (WCET), it uses probability distributions to model the execution times of subtasks (DAG nodes). We propose a new partitioning algorithm Least Latency Partitioned (LLP), which considers precedence constraints among nodes during the allocation process. Coupled with a scheduling algorithm that accounts for varying subtask criticality and constraints, the end-to-end latencies of safety-critical paths/nodes are then minimized. We use tasksets inspired by real robotics to demonstrate that our framework allows for efficient scheduling in complex computational pipelines, with more flexible representation of timing constraints, and ultimately, safety-performance tradeoffs.