Elaborating Team Roles for Artificial Intelligence-based Teammates in Human-AI Collaboration

Group Decision and Negotiation https://doi.org/10.1007/s10726-022-09792-z Elaborating Team Roles for Artificial Intelligence‑based Teammates in Human‑AI Collaboration Dominik Siemon1 Accepted: 20 June 2022 © The Author(s) 2022 Abstract The increasing importance of artificial intelligence (AI) in everyday work also means that new insights into team collaboration must be gained. It is important to research how changes in team composition affect joint work, as previous theories and insights on teams are based on the knowledge of pure human teams. Especially, when AI-based systems act as coequal partners in collaboration scenarios, their role within the team needs to be defined. With a multi-method approach including a quantitative and a qualitative study, we constructed four team roles for AIbased teammates. In our quantitative survey based on existing team role concepts (n = 1.358), we used exploratory and confirmatory factor analysis to construct possible roles that AI-based teammates can fulfill in teams. With nine expert interviews, we discussed and further extended our initially identified team roles, to construct consistent team roles for AI-based teammates. The results show four consistent team roles: the coordinator, creator, perfectionist and doer. The new team roles including their skills and behaviors can help to better design hybrid human-AI teams and to better understand team dynamics and processes. Keywords Human-AI collaboration · Collaboration · Artificial intelligence · Team roles · Team composition 1 Introduction The rapid development of artificial intelligence (AI) in recent years leads to an enormous potential for the entire value creation of organizations (Russell and Norvig 2020). High computing power and novel algorithms are used to evaluate large amounts of data, make profitable predictions or recognize patterns (Kaplan and Haenlein 2019). AI is a phenomenon or term that has been already used for a long time (McCarthy et al. 1955) and is seen as a process rather than a technology in its * Dominik Siemon 1 School of Engineering Science, LUT University, Mukkulankatu 19, 15210 Lahti, Finland 13 Vol.:(0123456789) D. Siemon own right. Berente et al. (2021) therefore define AI as "the frontier of computational advancements that references human intelligence" (p. 5), which led to several novel AI-based systems and applications, especially recently. AI-based systems subsequently take many forms, for example, as interactive actors with humans. Prominent systems are for example Apple’s Siri or Amazon’s Alexa, that are assisting users in the private sector, but also employees in organizational processes, for example in IT support (Maedche et al. 2016; Kaplan and Haenlein 2019; Morana et al. 2019). This increased implementation, usage, and the mere presence of such AI-based systems is changing the way we interact and co-exist with technology (Anderson et al. 2018; Kaplan and Haenlein 2019; Seeber et al. 2020; Mirbabaie et al. 2021). Today, computers are no longer perceived as mere tools, but as interaction and collaboration partners in mutual value creation (Nass and Moon 2000; Seeber et al. 2020; Mirbabaie et al. 2021). This is mainly due to the way they interact and communicate, namely in the most natural way possible. Improvements in methods of natural language processing led to the fact that computers are perceived as human and are therefore treated accordingly (Nass and Moon 2000; Epley et al. 2007; Qiu and Benbasat 2009). Not only language but general behavior and appearance play a decisive role in how we perceive and interact with them. Such AI-based systems become not only more intelligent but also more human-like with characteristics such as personality, autonomy, empathy, and emotion (Nass and Moon 2000; Epley et al. 2007; Qiu and Benbasat 2009; Ahmad et al. 2021). These characteristics distinguish AI-based systems as we define them, which simulate human intelligence with all its facets (personality, autonomy, empathy, and emotion), from past automation systems, which were primarily designed to process tasks in an intelligent and automated way (Berente et al. 2021). We therefore see AI-based systems as systems that reference human intelligence in all its facets, incorporating social behavioral patterns in order to be able to interact in collaborative processes. In this context, it is often said that AI will take over many jobs in the future because of its sheer power to perform tasks faster and more efficiently (Aleksander 2017; Anderson et al. 2018; Schwartz et al. 2019). However, many researchers argue that “humans and computers have complementary capabilities that can be combined to augment each other” (Dellermann et al. 2019, p. 4). Concepts such as hybrid intelligence, human-AI symbiosis, or humanin-the-loop argue that superior results can be accomplished when combining the capabilities of humans and AI in mutual value generation, by continuously learning from each other and improving each other (Dellermann et al. 2019; Gerber et al. 2020). The main aspect of these concepts is that tasks are performed collectively, and dependent activities are coordinated. If these mutual activities are now used to achieve a common goal, AI-based systems become team members in a collaboration scenario (Siemon et al. 2018; Seeber et al. 2020; Mirbabaie et al. 2021). As a result, the collaboration between humans and AI-based systems arises, which changes the way teams work together. This leads to new workplace configurations where autonomous AI-based systems jointly work within a team, fulfill certain roles and take over interdependent tasks (Bittner et al. 2019; Seeber et al. 2020). Accordingly, established theories on group phenomena and processes from team, organization, and group research have to be reflected, reconsidered or even 13 Elaborating Team Roles for Artificial Intelligence‑based… completely overthrown (Krämer et al. 2012; Seeber et al. 2020). Although, research has shown that traditional social responses and team dynamics can be applied to human-AI collaboration, as there are “more similarities between human–human and human–machine interactions than differences” (Krämer et al. 2012, p. 233), still many aspects of human-AI collaboration need to be further investigated. In addition to aspects that have recently been researched more frequently, such as trust in AI (Elson et al. 2020; Jessup et al. 2020), forms of reciprocity in human-AI collaboration (Goodman et al. 2016), or anthropomorphism (Qiu and Benbasat 2009; Araujo 2018; Watson 2019), team composition, and in specific, the potential roles of AIbased systems within a team are crucial for future human-AI collaboration. Research from outside the core of information systems often focuses on certain tasks (Daugherty and Wilson 2018) or even jobs (Morini-Bianzino, 2017) that AIbased systems can or will fulfill in future work scenarios. Nevertheless, this philosophy still limits AI to t (...truncated)