AI vs. Human: The Public's Perceptions of the Design Abilities of Artificial Intelligence
Year: 2023
Editor: Kevin Otto, Boris Eisenbart, Claudia Eckert, Benoit Eynard, Dieter Krause, Josef Oehmen, Nad
Author: Chong, Leah (1); Yang, Maria (2)
Series: ICED
Institution: 1: Massachusetts Institute of Technology;
2: Massachusetts Institute of Technology
Section: Design Methods
Page(s): 0495-0504
DOI number: https://doi.org/10.1017/pds.2023.50
ISBN: -
ISSN: -
Abstract
With the increasing implementation of artificial intelligence (AI) in the design process, it is crucial to understand how users will accept AI-designed products. This work studies how the public currently perceives an AI's design capability as compared to a human designer's capability by conducting an online survey of 205 people via Amazon Mechanical Turk. The survey collects the respondents' perception on 16 specific bicycle design goals, demographic information, and self-reported level of design and AI/ML knowledge. Findings reveal that people think an AI would perform worse than a human designer on most design goals, particularly the goals that are user-dependent. This work also shows that the higher people's self-reported level of knowledge in design and the older they are, the more likely they are to think an AI's design capability would exceed a human designer's capability. The insights from this work add to the understanding of user acceptance of AI-designed products, as well as human designers' acceptance of AI input in human-AI teams.
Keywords: Artificial intelligence, Collaborative design, Market implications