Short Paper: Swarm Intelligence Amplifies the IQ of Collaborating Teams

Willcox, Gregg; Rosenberg, Louis

doi:10.1109/ai4i46381.2019.00036

Cited by 7 publications

(2 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Empirical findings suggest that incorporating AI into business teams can enhance productivity (Wilcox & Rosenberg, 2019). As such, managers must carefully identify not only the types of tasks but also the types of groups that are most likely to benefit from human–AI collaboration.…”

Section: Implications For Practicementioning

confidence: 99%

A multilevel review of artificial intelligence in organizations: Implications for organizational behavior research and practice

et al. 2023

View full text Add to dashboard Cite

SummaryThe rising use of artificially intelligent (AI) technologies, including generative AI tools, in organizations is undeniable. As these systems become increasingly integrated into organizational practices and processes, understanding their impact on workers' experiences and job designs is critical. However, the ongoing discourse surrounding AI use in the workplace remains divided. Proponents of the technology extol its benefits for enhancing efficiency and productivity, while others voice concerns about the potential harm to human workers. To provide greater clarity on this pressing issue, this article presents a systematic review of empirical research that sheds light on the implications of AI use at work. Organized under five inductively generated themes within a multilevel framework, we uncover individual, group, and organizational factors that shape the interplay between humans and AI. Specifically, the themes are: (1) human–AI collaboration; (2) perceptions of algorithmic and human capabilities; (3) worker attitudes towards AI; (4) AI as a control mechanism in algorithmic management of platform‐based work; and (5) labor market implications of AI use. Our review offers insights into these themes and identifies five pathways for future research. Finally, we provide practical recommendations for organizational leaders seeking to implement AI technologies while prioritizing their employees' well‐being.

show abstract

Section: Implications For Practicementioning

confidence: 99%

A multilevel review of artificial intelligence in organizations: Implications for organizational behavior research and practice

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Studies in the past have shown how collaboration has led to improved outcomes like reading radiology images (Wang et al. , 2016), an increase in IQ in business teams (Wilcox and Rosenberg, 2019), improved team performance of human players in the online game (Shirado and Christakis, 2017), increased efficiency of different processes in organizations, which include decision-making, greater flexibility in systems, increase in the scale of actions of the organization and personalization of information (Wilson and Daugherty, 2018). While there is great appreciation for the potential of AI to revolutionize organizations, there is some level of uncertainty about its consequences on people and organizations (Bedué and Fritzsche, 2022).…”

Section: Past Literaturementioning

confidence: 99%

Effective human–AI work design for collaborative decision-making

Jain

Garg

Khera

2022

View full text Add to dashboard Cite

PurposeWith the increase in the adoption of artificial intelligence (AI)-based decision-making, organizations are facilitating human–AI collaboration. This collaboration can occur in a variety of configurations with the division of labor, with differences in the nature of interdependence being parallel or sequential, along with or without the presence of specialization. This study intends to explore the extent to which humans express comfort with different models human–AI collaboration.Design/methodology/approachSituational response surveys were adopted to identify configurations where humans experience the greatest trust, role clarity and preferred feedback style. Regression analysis was used to analyze the results.FindingsSome configurations contribute to greater trust and role clarity with AI as a colleague. There is no configuration in which AI as a colleague produces lower trust than humans. At the same time, the human distrust in AI may be less about human vs AI and more about the division of labor in which human–AI work.Practical implicationsThe study explores the extent to which humans express comfort with different models of an algorithm as partners. It focuses on work design and the division of labor between humans and AI. The finding of the study emphasizes the role of work design in human–AI collaboration. There is human–AI work design that should be avoided as they reduce trust. Organizations need to be cautious in considering the impact of design on building trust and gaining acceptance with technology.Originality/valueThe paper's originality lies in focusing on the design of collaboration rather than on performance of the team.

show abstract