Collaboration with Intelligent Systems: Machines as Teammates

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    Implementing an Intelligent Collaborative Agent as Teammate in Collaborative Writing: toward a Synergy of Humans and AI
    ( 2021-01-05) Wiethof, Christina ; Tavanapour, Navid ; Bittner, Eva
    This paper aims at implementing a hybrid form of group work through the incorporation of an intelligent collaborative agent into a Collaborative Writing process. With that it contributes to the overall research gap establishing acceptance of AI towards complementary hybrid work. To approach this aim, we follow a Design Science Research process. We identify requirements for the agent to be considered a teammate based on expert interviews in the light of Social Response Theory and the concept of the Uncanny Valley. Next, we derive design principles for the implementation of an agent as teammate from the collected requirements. For the evaluation of the design principles and the human teammates’ perception of the agent, we instantiate a Collaborative Writing process via a web-application incorporating the agent. The evaluation reveals the partly successful implementation of the developed design principles. Additionally, the results show the potential of hybrid collaboration teams accepting non-human teammates.
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    Human-AI Collaboration in Healthcare: A Review and Research Agenda
    ( 2021-01-05) Lai, Yi ; Kankanhalli, Atreyi ; Ong, Desmond
    Advances in Artificial Intelligence (AI) have led to the rise of human-AI collaboration. In healthcare, such collaboration could mitigate the shortage of qualified healthcare workers, assist overworked medical professionals, and improve the quality of healthcare. However, many challenges remain, such as investigating biases in clinical decision-making, the lack of trust in AI and adoption issues. While there is a growing number of studies on the topic, they are in disparate fields, and we lack a summary understanding of this research. To address this issue, this study conducts a literature review to examine prior research, identify gaps, and propose future research directions. Our findings indicate that there are limited studies about the evolving and interactive collaboration process in healthcare, the complementarity of humans and AI, the adoption and perception of AI, and the long-term impact on individuals and healthcare organizations. Additionally, more theory-driven research is needed to inform the design, implementation, and use of collaborative AI for healthcare and to realize its benefits.
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    Hello World! I am Charlie, an Artificially Intelligent Conference Panelist
    ( 2021-01-05) Cummings, Patrick ; Mullins, Ryan ; Moquete, Manuel ; Schurr, Nathan
    In recent years, advances in artificial intelligence (AI) have far outpaced our ability to understand and leverage them. In no domain has this been more true than in conversational agents (CAs). Transformer-based generative language models, such as GPT-2, significantly advance CAs' ability to generate creative and relevant content. It is critical to start exploring collaboration with these CAs. In this paper, we focus on an initial step by enabling a human-augmented, AI-driven CA to contribute to a panel discussion. Key questions include training a transformer-based AI to talk like a panelist, effectively embodying the CA to interact with panel participants, and defining the operational requirements and challenges to a CA gaining acceptance from its peers. Our results highlight the benefits that varied training, equal and dynamic representation, and fluid operation can have for AI applications. While acknowledging limitations, we present a path forward to richer, more natural human-AI collaboration.
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    Digital Facilitation Assistance for Collaborative, Creative Design Processes
    ( 2021-01-05) Bittner, Eva ; Mirbabaie, Milad ; Morana, Stefan
    People focus more and more on creating innovations collaboratively. Digital assistants (DAs) can accelerate such collaborative, creative design processes by supporting people in their work. Especially in the context of design, such as design thinking, moderators that facilitate collaborative, creative workshops can benefit from the support for their teams and themselves in the form of a DA. Based on interviews with experienced workshop facilitators from research and practice, we discuss implications for the design and usage of DAs in collaborative, creative design processes. We identify 16 distinct capabilities of DAs for task, process and interaction facilitation to guide design research and practitioners’ endeavors toward helpful automated DT facilitation support. Moreover, we outline a research agenda to foster future research on this young research area.
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    Between Anthropomorphism, Trust, and the Uncanny Valley: a Dual-Processing Perspective on Perceived Trustworthiness and Its Mediating Effects on Use Intentions of Social Robots
    ( 2021-01-05) Nissen, Anika ; Jahn, Katharina
    Designing social robots with the aim to increase their acceptance is crucial for the success of their implementation. However, even though increasing anthropomorphism is often seen as a promising way to achieve this goal, the uncanny valley effect proposes that anthropomorphism can be detrimental to acceptance unless robots are almost indistinguishable from humans. Against this background, we use a dual processing theory approach to investigate whether an uncanny valley of perceived trustworthiness (PT) can be observed for social robots and how this effect differs between the intuitive and deliberate reasoning system. The results of an experiment with four conditions and 227 participants provide support for the uncanny valley effect. Furthermore, mediation analyses suggested that use intention decreases through both reduced intuitive and deliberate PT for medium levels of anthropomorphism. However, for high levels of anthropomorphism (indistinguishable from real human), only intuitive PT determined use intention. Consequently, our results indicate both advantages and pitfalls of anthropomorphic design.
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    Introduction to the Minitrack on Collaboration with Intelligent Systems: Machines as Teammates
    ( 2021-01-05) Derrick, Douglas ; Seeber, Isabella ; Elson, Joel ; Waizenegger, Lena