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Design and Trust in Multi-AI Team Collaboration Systems

  • 간행물
    한국경영정보학회 정기 학술대회 바로가기
  • 권호(발행년)
    2024 KMIS International Conference 추계국제학술대회 (2024.11) 바로가기
  • 페이지
    pp.289-291
  • 저자
    Ang Zeng, Xusen Cheng, Kanliang Wang
  • 언어
    영어(ENG)
  • URL
    https://www.earticle.net/Article/A472520

원문정보

초록

영어
While generative artificial intelligence (GAI) has shown promise in various domains, it faces challenges such as knowledge deficits and hallucination issues. To tackle these problems, we propose a multi-AI collaboration system. Our research employs a mixed-methods approach, combining Design Science Research (DSR) with quantitative and qualitative methods. We investigate the design principles for efficient multi-AI collaboration systems and their impact on team trust compared to single AI collaboration. The results indicate that multi-AI collaboration systems significantly enhance users' trust in AI, particularly in the perception of capability. Furthermore, a number of key design principles for multi-AI collaboration systems were identified. These findings contribute to the development of more effective and trustworthy AI collaboration systems, paving the way for improved human-AI teamwork.

목차

Abstract
Introduction
Methods
Findings and Future Directions
Acknowledgments
References

저자

  • Ang Zeng [ School of Information, Renmin University of China ]
  • Xusen Cheng [ School of Information, Renmin University of China ] Corresponding Author
  • Kanliang Wang [ School of business, Renmin University of China ]

참고문헌

자료제공 : 네이버학술정보

    간행물 정보

    • 간행물
      한국경영정보학회 정기 학술대회 [KMIS Conference]
    • 간기
      반년간
    • 수록기간
      1990~2025
    • 십진분류
      KDC 325 DDC 658