Decision Making Strategies and Team Efficacy in Human-AI Teams-Reference-Cited by-同舟云学术

Decision Making Strategies and Team Efficacy in Human-AI Teams

Published:2023-04-14 Issue:CSCW1 Volume:7 Page:1-24
ISSN:2573-0142
Container-title:Proceedings of the ACM on Human-Computer Interaction
language:en
Short-container-title:Proc. ACM Hum.-Comput. Interact.

Author:

Munyaka Imani¹^ORCID,Ashktorab Zahra²^ORCID,Dugan Casey³^ORCID,Johnson J.³^ORCID,Pan Qian³^ORCID

Affiliation:

1. University of California San Diego, San Diego, CA, USA

2. IBM Research, Yorktown Heights, NY, USA

3. IBM Research, Cambridge, MA, USA

Abstract

Human-AI teams are increasingly prevalent in various domains. We investigate how the decision-making of a team member in a human-AI team impacts the outcome of the collaboration and perceived team-efficacy. In a large scale study on Mechanical Turk (n=125), we find significant differences across different decision making styles and disclosed AI identity disclosure in an AI-driven collaborative game. We find that autocratic decision-making negatively impacts team-efficacy in Human-AI teams, similar to its effects on human-only teams. We find that decision making style and AI-identity disclosure impacts how individuals make decisions in a collaborative context. We discuss our findings of the differences of collaborative behavior in human-human-AI teams and human-AI-AI teams.

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Networks and Communications,Human-Computer Interaction,Social Sciences (miscellaneous)

Link

https://dl.acm.org/doi/pdf/10.1145/3579476

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