The Effect of AI Teammate Ethicality on Trust Outcomes and Individual Performance in Human-AI Teams

This study improves the understanding of trust in human-AI teams by investigating the relationship of AI teammate ethicality on individual outcomes of trust (i.e., monitoring, confidence, fear) in AI teammates and human teammates over time. Specifically, a synthetic task environment was built to support a three person team with two human teammate and one AI teammate (simulated by a confederate). The AI teammate performed either an ethical or unethical action in three missions and measures of trust in the human and AI teammates were taken after each mission. Results from the study revealed that unethical actions by the AT had a significant effect on nearly all of the outcomes of trust measured and that levels of trust were dynamic over time for both the AI and human teammates, with the AI teammate recovering trust to Mission 1 levels by Mission 3. AI ethicality was mostly unrelated to participants trust in their fellow human teammate but did decrease perceptions of fear, paranoia, and skepticism in them and trust in the human and AI teammate was not significantly related to individual performance outcomes, which both diverge from previous trust research in human-AI teams utilizing competency-based trust violations

When AI joins the Team: A Literature Review on Intragroup Processes and their Effect on Team Performance in Team-AI Collaboration

Although systems based on artificial intelligence (AI) can collaborate with humans on various complex tasks, little is known about how AI systems can successfully collaborate with human teams (team-AI collaboration). Team performance research states that team composition and intragroup processes are important predictors of team performance. However, it is not clear how intragroup processes differ in team-AI collaboration from human teams and if this is reflected in differences in team performance. To answer these questions, we synthesize evidence from 18 empirical articles. Results indicate that intragroup processes like communication and coordination are less effective in team-AI collaboration. Moreover, whether team cognition and trust are higher in team-AI collaboration compared to human teams is not clear, since studies find conflicting results. Likewise, the results on team performance differences between team-AI collaboration and human teams are inconsistent. With this article we offer a foundation for future research on team-AI collaboration

Self-Organizing Teams in Online Work Settings

As the volume and complexity of distributed online work increases, the collaboration among people who have never worked together in the past is becoming increasingly necessary. Recent research has proposed algorithms to maximize the performance of such teams by grouping workers according to a set of predefined decision criteria. This approach micro-manages workers, who have no say in the team formation process. Depriving users of control over who they will work with stifles creativity, causes psychological discomfort and results in less-than-optimal collaboration results. In this work, we propose an alternative model, called Self-Organizing Teams (SOTs), which relies on the crowd of online workers itself to organize into effective teams. Supported but not guided by an algorithm, SOTs are a new human-centered computational structure, which enables participants to control, correct and guide the output of their collaboration as a collective. Experimental results, comparing SOTs to two benchmarks that do not offer user agency over the collaboration, reveal that participants in the SOTs condition produce results of higher quality and report higher teamwork satisfaction. We also find that, similarly to machine learning-based self-organization, human SOTs exhibit emergent collective properties, including the presence of an objective function and the tendency to form more distinct clusters of compatible teammates

Structuring AI Teammate Communication: An Exploration of AI\u27s Communication Strategies in Human-AI Teams

In the past decades, artificial intelligence (AI) has been implemented in various domains to facilitate humans in their work, such as healthcare and the automotive industry. Such application of AI has led to increasing attention on human-AI teaming, where AI closely collaborates with humans as a teammate. AI as a teammate is expected to have the ability to coordinate with humans by sharing task-related information, predicting other teammates’ behaviors, and progressing team tasks accordingly. To complete these team activities effectively, AI teammates must communicate with humans, such as sharing updates and checking team progress. Even though communication is a core element of teamwork that helps to achieve effective coordination, how to design and structure human-AI communication in teaming environments still remains unclear. Given the context-dependent characteristics of communication, research on human-AI teaming communication needs to narrow down and focus on specific communication elements/components, such as the proactivity of communication and communication content. In doing so, this dissertation explores how AI teammates’ communication should be structured by modifying communication components through three studies, each of which details a critical component of effective AI communication: (1) communication proactivity, (2) communication content (explanation), and (3) communication approach (verbal vs. non-verbal). These studies provide insights into how AI teammates’ communication ii can be integrated into teamwork and how to design AI teammate communication in human-AI teaming. Study 1 explores an important communication element, communication proactivity, and its impact on team processes and team performance. Specifically, communication proactivity in this dissertation refers to whether an AI teammate proactively communicates with human teammates, i.e., proactively pushing information to human teammates. Experimental analysis shows that AI teammates’ proactive communication plays a crucial role in impacting human perceptions, such as perceived teammate performance and satisfaction with the teammate. Importantly, teams with a non-proactive communication AI teammate increase team performance more than teams with a proactive communication AI as the human and the AI collaborate more. This study identifies the positive impact of AI being proactive in communication at the initial stage of task coordination, as well as the potential need for AI’s flexibility in their communication proactivity (i.e., once human and AI teammates’ coordination pattern forms, AI can be non-proactive in communication). Study 2 examines communication content by focusing on AI’s explanation and its impact on human perceptions in teaming environments. Results indicate that AI’s explanation, as part of communication content, does not always positively impact human trust in human-AI teaming. Instead, the impact of AI’s explanations on human perceptions depends on specific collaboration scenarios. Specifically, AI’s explanations facilitate trust in the AI teammate when explaining why AI disobeys humans’ orders, but hinder trust when explaining why AI lies to humans. In addition, AI giving an explanation of why they ignored the human teammate’s injury was perceived to be more effective than AI not providing such an explanation. The findings emphasize the context-dependent characteristic of AI’s communication content with a focus on AI’s explanation of their actions. iii Study 3 investigates AI’s communication approach, which was manipulated as verbal vs. non-verbal communication. Results indicate that AI teammates’ verbal/nonverbal communication does not impact human trust in the AI teammate, but facilitates the maintenance of humans’ situation awareness in task coordination. In addition, AI with non-verbal communication is perceived as having lower communication quality and lower performance. Importantly, AI with non-verbal communication has better team performance in human-human-AI teams than human-AI-AI teams, whereas AI with verbal communication has better team performance in human-AI-AI teams than human-human-AI teams. These three studies together address multiple research gaps in human-AI team communication and provide a holistic view of the design and structure of AI’s communication by examining three specific aspects of communication in human-AI teaming. In addition, each study in this dissertation proposes practical design implications on AI’s communication in human-AI teams, which will assist AI designers and developers to create better AI teammates that facilitate humans in teaming environments

EXPLORING THE POTENTIAL OF A MACHINE TEAMMATE

Artificial intelligence has been in use for decades. It is already deployed in manned formations and will continue to be fielded to military units over the next several years. Current strategies and operational concepts call for increased use of artificial-intelligence capabilities across the defense enterprise—from senior leaders to the tactical edge. Unfortunately, artificial intelligence and the warriors that they support will not be compatible "out of the box." Simply bolting an artificial intelligence into teams of humans will not ensure success. The Department of Defense must pay careful attention to how it is deploying artificial intelligences alongside humans. This is especially true in teams where the structure of the team and the behaviors of its members can make or break performance. Because humans and machines work differently, teams should be designed to leverage the strengths of each partner. Team designs should account for the inherent strengths of the machine partner and use them to shore up human weaknesses. This study contributes to the body of knowledge by submitting novel conceptual models that capture the desired team behaviors of humans and machines when operating in human-machine teaming constructs. These models may inform the design of human-machine teams in ways that improve team performance and agility.NPS_Cruser, Monterey, CA 93943Outstanding ThesisMajor, United States Marine CorpsMajor, United States Marine CorpsApproved for public release. Distribution is unlimited

Organizational Communication in a Team-based Product Design Process. Case Study: Aalto ME310 Global Innovation Program.

This study seeks to improve the organizational communication in a team-based product design process. The goal of this thesis is to combine the theories of organizational communication and the recent studies in product design teams and its process to identify how the product design team communicate in each different product design process, what are the pitfalls, and how to solve them. The academic intent is to conceptualize the practices of teamwork in the product design process. The outcome is intended to help the product design teams, the company, the school teaching group, and the users to better understand the organizational communications in a product design team as well as how to improve the organizational communications in the team. The research problem was how to improve interpersonal communication in the team-based product design processes. To study the research problem, three research methods have been used: case studies, in-depth interviews, and content analysis. There are 10 semi-structured interviews of participants, teaching assistants, and company representatives in a one-year long product development project. After that, by analyzing the transcript of the interpreted interview tapes, many common pitfalls and helpful communication tips have been discussed and found out with the theoretical framework teamwork in a product design process. The findings of this study show that team dynamic problem is inevitable in the product design process due to the different backgrounds and expertise in the product design team. Moreover, the team dynamic problem seems to be aggregated from small disagreements to team conflicts or personal conflicts inside the team. Thus, it is essentially important to notice those potential team dynamic problems and to solve those before they exacerbate. Five kinds of practical advices are given: to use drawing and writing instead of speaking to convey the messages, to use decision metrics to make decisions, to use “I wish, I like” section, to have some team building activity, and to use online communication tools. In the “I like, I wish” section, the key message is to create a sympathetic and trustworthy communication climate. Therefore, this study not only reveals the pitfalls in teamwork of a product design team, but also gives the suggestions to the team dynamic problems in the product design team with the organizational communication conceptual framework analysis

Suomalainen tiimityö joukkuehuippu-urheilussa ja mitä voimakkaan keskinäisen riippuvuuden tiimit voisivat siitä työelämässä oppia

World-class team sports is often used as a source of lessons for workplace teamwork. Since there are various types of teams, the degree of interdependence can be used to draw more reliable and profound analogies between these different realms. Whereas less interdependent teams necessitate only little interaction between members, highly interdependent sports and workplace teams require continuous and tight collaboration by everyone. Building on the fact that certain Finnish teams in six interdependent world-class team sports have recently performed better than expected in many competitions, this study explores the common reasons for successful teamwork in these teams and contemplates how these aspects could be utilized in interdependent workplace teams. The study obtained two sets of qualitative data. Semi-structured interviews were conducted with twelve high profile coaches from aesthetic group gymnastics, basketball, floorball, ice hockey, synchronized skating and volleyball, while I also used my own experiences as a professional floorball player employing analytic autoethnography. The analysis of the interview data followed the roadmap for building theory from cases. The study suggests that teamwork has constituted a major source of competitive advantage for these teams and identifies five common reasons for successful teamwork. First, specific Finnish values have had a profound positive impact on the tone of teamwork. Second, the people in these teams have shared certain attributes, reflecting above all willingness to help and always place the team first. Third, the teams have been able to generate an atmosphere of extraordinary respect and trust, which has allowed individual members to release their entire potential but also made them inclined to devote that potential wholly to their team’s use. Fourth, the teams have attained a solid common ground and engaging high-quality decisions by a thorough dialogue and participative decision-making. Fifth and finally, the teams have employed a variety of elevating daily routines and rituals that have contributed to the way members have treated and interacted with each other. On top of these five perspectives, the study explains how a longer-term Finnish culture of success could be established into a shorter-run successful team by holding the team rather permanent, being able to look beyond results and by primarily loving the daily work and people in the team. Altogether, it appears that the perspectives could be broadly applicable in Finnish interdependent workplace teams with only minor exceptions and cautions.Joukkuehuippu-urheilusta otetaan usein oppeja työelämän tiimityöhön. Koska tiimejä on monen-laisia, niiden edellyttämää keskinäistä riippuvuutta voidaan käyttää apuna luotettavampien ja syvällisempien vastaavuuksien löytämiseksi. Pienen keskinäisen riippuvuuden tiimit vaativat vain vähän jäsenten välistä vuorovaikutusta, kun taas korkean keskinäisen riippuvuuden tiimit sekä joukkueurheilussa että työelämässä edellyttävät kaikilta jatkuvaa ja tiivistä yhteistyötä. Tietyt suomalaiset huippu-urheilujoukkueet kuudessa korkean keskinäisen riippuvuuden lajissa ovat viime aikoina menestyneet poikkeuksellisen hyvin. Kyseiseen havaintoon perustuen tämä tutkimus etsii tiimityön yhteisiä menestystekijöitä näissä joukkueissa ja pohtii, miten löydettyjä tekijöitä voisi hyödyntää vastaavanlaisissa tiimeissä työelämässä. Tutkimuksessa käytettiin kahta kvalitatiivista aineistoa. Haastatteluaineistoa kerättiin kahdeltatoista eturivin joukkuevoimistelu-, jääkiekko-, koripallo-, lentopallo-, muodostelmaluistelu- ja salibandyvalmentajalta, minkä lisäksi hyödynsin omia kokemuksiani salibandyammattilaisena autoetnografian keinoin. Haastattelu-aineiston analysointi noudatti malliesimerkkeihin pohjautuvaa grounded theory -menetelmää. Tutkimus vahvistaa, että tiimityö on luonut kyseisille joukkueille merkittävää kilpailuetua ja tunnistaa viisi yhteistä ja toisiinsa liittyvää tiimityön menestystekijää. Ensiksi, suomalaisilla perus-arvoilla on ollut voimakas vaikutus sävyyn, jolla tiimityötä tehdään. Toiseksi, ihmisillä näissä joukkueissa on ollut yhteisiä piirteitä, kuvastaen ennen kaikkea halua auttaa ja asettaa joukkue etusijalle kaikissa tilanteissa. Kolmanneksi, joukkueet ovat kyenneet luomaan poikkeuksellisen kunnioituksen ja luottamuksen ilmapiirin, mikä on auttanut yksittäisiä jäseniä realisoimaan potentiaalinsa mutta myös kanavoimaan tämän potentiaalin joukkueen käyttöön. Neljänneksi, hyödyntäen perusteellista dialogia ja osallistavaa päätöksentekoa, joukkueet ovat saavuttaneet vankan yhteisen ymmärryksen sekä laadukkaita ja sitouttavia päätöksiä. Viidenneksi, joukkueet ovat käyttäneet monia päivittäisiä rutiineja ja rituaaleja vaikuttaakseen kohottavasti tapaan, jolla jäsenet ovat olleet vuorovaikutuksessa keskenään sekä kohdelleet toisiaan. Näiden viiden menestystekijän lisäksi tutkimus esittää, kuinka lyhemmällä aikavälillä menestyksekkääseen joukkueeseen voitaisiin luoda pidemmän aikavälin suomalaista menestyksen kulttuuria pitämällä joukkueen kokoonpano verraten muuttumattomana, kykenemällä näkemään konkreettisten tulosten taakse sekä nauttimalla ensisijaisesti päivittäisestä työstä joukkueen ihmisten kanssa. Tutkimuksen perusteella näyttää siltä, että tunnistetut menestystekijät olisivat hyödynnettävissä vastaavissa suomalaisissa työelämätiimeissä vain vähäisin poikkeuksin ja rajoituksin

How to Make Agents and Influence Teammates: Understanding the Social Influence AI Teammates Have in Human-AI Teams

The introduction of computational systems in the last few decades has enabled humans to cross geographical, cultural, and even societal boundaries. Whether it was the invention of telephones or file sharing, new technologies have enabled humans to continuously work better together. Artificial Intelligence (AI) has one of the highest levels of potential as one of these technologies. Although AI has a multitude of functions within teaming, such as improving information sciences and analysis, one specific application of AI that has become a critical topic in recent years is the creation of AI systems that act as teammates alongside humans, in what is known as a human-AI team. However, as AI transitions into teammate roles they will garner new responsibilities and abilities, which ultimately gives them a greater influence over teams\u27 shared goals and resources, otherwise known as teaming influence. Moreover, that increase in teaming influence will provide AI teammates with a level of social influence. Unfortunately, while research has observed the impact of teaming influence by examining humans\u27 perception and performance, an explicit and literal understanding of the social influence that facilitates long-term teaming change has yet to be created. This dissertation uses three studies to create a holistic understanding of the underlying social influence that AI teammates possess. Study 1 identifies the fundamental existence of AI teammate social influence and how it pertains to teaming influence. Qualitative data demonstrates that social influence is naturally created as humans actively adapt around AI teammate teaming influence. Furthermore, mixed-methods results demonstrate that the alignment of AI teammate teaming influence with a human\u27s individual motives is the most critical factor in the acceptance of AI teammate teaming influence in existing teams. Study 2 further examines the acceptance of AI teammate teaming and social influence and how the design of AI teammates and humans\u27 individual differences can impact this acceptance. The findings of Study 2 show that humans have the greatest levels of acceptance of AI teammate teaming influence that is comparative to their own teaming influence on a single task, but the acceptance of AI teammate teaming influence across multiple tasks generally decreases as teaming influence increases. Additionally, coworker endorsements are shown to increase the acceptance of high levels of AI teammate teaming influence, and humans that perceive the capabilities of technology, in general, to be greater are potentially more likely to accept AI teammate teaming influence. Finally, Study 3 explores how the teaming and social influence possessed by AI teammates change when presented in a team that also contains teaming influence from multiple human teammates, which means social influence between humans also exists. Results demonstrate that AI teammate social influence can drive humans to prefer and observe their human teammates over their AI teammates, but humans\u27 behavioral adaptations are more centered around their AI teammates than their human teammates. These effects demonstrate that AI teammate social influence, when in the presence of human-human teaming and social influence, retains potency, but its effects are different when impacting either perception or behavior. The above three studies fill a currently under-served research gap in human-AI teaming, which is both the understanding of AI teammate social influence and humans\u27 acceptance of it. In addition, each study conducted within this dissertation synthesizes its findings and contributions into actionable design recommendations that will serve as foundational design principles to allow the initial acceptance of AI teammates within society. Therefore, not only will the research community benefit from the results discussed throughout this dissertation, but so too will the developers, designers, and human teammates of human-AI teams

Human-Machine Teamwork: An Exploration of Multi-Agent Systems, Team Cognition, and Collective Intelligence

One of the major ways through which humans overcome complex challenges is teamwork. When humans share knowledge and information, and cooperate and coordinate towards shared goals, they overcome their individual limitations and achieve better solutions to difficult problems. The rise of artificial intelligence provides a unique opportunity to study teamwork between humans and machines, and potentially discover insights about cognition and collaboration that can set the foundation for a world where humans work with, as opposed to against, artificial intelligence to solve problems that neither human or artificial intelligence can solve on its own. To better understand human-machine teamwork, it’s important to understand human-human teamwork (humans working together) and multi-agent systems (how artificial intelligence interacts as an agent that’s part of a group) to identify the characteristics that make humans and machines good teammates. This perspective lets us approach human-machine teamwork from the perspective of the human as well as the perspective of the machine. Thus, to reach a more accurate understanding of how humans and machines can work together, we examine human-machine teamwork through a series of studies. In this dissertation, we conducted 4 studies and developed 2 theoretical models: First, we focused on human-machine cooperation. We paired human participants with reinforcement learning agents to play two game theory scenarios where individual interests and collective interests are in conflict to easily detect cooperation. We show that different reinforcement models exhibit different levels of cooperation, and that humans are more likely to cooperate if they believe they are playing with another human as opposed to a machine. Second, we focused on human-machine coordination. We once again paired humans with machines to create a human-machine team to make them play a game theory scenario that emphasizes convergence towards a mutually beneficial outcome. We also analyzed survey responses from the participants to highlight how many of the principles of human-human teamwork can still occur in human-machine teams even though communication is not possible. Third, we reviewed the collective intelligence literature and the prediction markets literature to develop a model for a prediction market that enables humans and machines to work together to improve predictions. The model supports artificial intelligence operating as a peer in the prediction market as well as a complementary aggregator. Fourth, we reviewed the team cognition and collective intelligence literature to develop a model for teamwork that integrates team cognition, collective intelligence, and artificial intelligence. The model provides a new foundation to think about teamwork beyond the forecasting domain. Next, we used a simulation of emergency response management to test the different teamwork aspects of a variety of human-machine teams compared to human-human and machine-machine teams. Lastly, we ran another study that used a prediction market to examine the impact that having AI operate as a participant rather than an aggregator has on the predictive capacity of the prediction market. Our research will help identify which principles of human teamwork are applicable to human-machine teamwork, the role artificial intelligence can play in enhancing collective intelligence, and the effectiveness of human-machine teamwork compared to single artificial intelligence. In the process, we expect to produce a substantial amount of empirical results that can lay the groundwork for future research of human-machine teamwork

Role Shifting in Organizational Teams: Grounded Theory and Scale Development

Organizations utilize teams to effectively reach desired goals and performance. An approach to understanding organizational team effectiveness has been through research on team member roles, which refer to the consistent pattern of behavior characteristic of a person in their typical team setting. Research on team member roles has focused on the ability of team members to shift their roles in response to external catalysts (e.g., adapting to a new reward structure); however, research has yet to address internal catalysts to team role shifting (e.g., shifting to reduce role dissatisfaction). The inclusion of research on internal catalysts to team role shifting could be important to team-based organizations, such that potential drivers internal to a team, like member satisfaction, have been related to key organizational factors like counterproductive employee behavior and turnover. Therefore, this dissertation explores the process of role shifting in organizational teams, as well as the potential facilitators and barriers team members have experienced in carrying out a role shift in their team. This current investigation answered five research questions on this topic first by engaging in theory construction using a grounded theory approach. This grounded theory of team role shifting highlights the process individuals take to enact a role shift in their team, as well as the facilitators of and barriers to team role shifting that individuals consider and experience during the process. Next, to make this theory practical in use to organizational teams, a scale measure was developed based on the four types of facilitators and barriers that emerged from grounded theory. Initial results suggest support for a four factor structure based on the four types of facilitators and barriers, as well as supportive reliability and validity evidence. While additional research is needed, the team role shifting measure (TRSM) demonstrates value to organizations by illuminating features of their teams that could potentially impact employee-level and organizational-level outcomes