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Human-AI collaboration by design

Published online by Cambridge University Press:  16 May 2024

Binyang Song ,
Qihao Zhu  and
Jianxi Luo
Binyang Song*
Affiliation:
Virginia Tech, United States of America
Qihao Zhu
Affiliation:
Singapore University of Technology and Design, Singapore
Jianxi Luo
Affiliation:
Singapore University of Technology and Design, Singapore
*
binyangs@vt.edu

Abstract

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Human-AI collaboration (HAIC) is a promising strategy to transform engineering design and innovation, yet how to design artificial intelligence (AI) to boost HAIC remains unclear. Accordingly, this paper provides a new, unified, and comprehensive scheme for classifying AI roles. On this basis, we develop an AI design framework that outlines expected AI capabilities, interactive attributes, and trust enablers across various HAIC scenarios, offering guidance for integrating AI into human teams effectively. We also discuss current advancements, challenges, and prospects for future research.

Keywords

collaborative designartificial intelligence (AI)innovation

Information

Type
Artificial Intelligence and Data-Driven Design
Information
Proceedings of the Design Society , Volume 4: DESIGN 2024 , May 2024 , pp. 2247 - 2256
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2024.

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