E&PDE 2024 - 26th International Conference on Engineering & Product Design Education
The Conference will bring together representatives from education and industry interested in sharing new perspectives on design education.
Knowledge Base Repository
In addition to research papers, the Design Society is developing several valuable resources for those interested in the study of design. These include a repository of PhD theses, a library of case studies and transcripts of design activities, and an archive of our newsletters. Please note that these resources are accessible exclusively to Design Society members.
UNDERSTANDING HOW UNDERGRADUATE STUDENTS PERCEIVE BIASES IN AI-GENERATED IMAGES, A RESEARCH-THROUGH-DESIGN PROBE
Silva, Veronica; Buzzo, Daniel; Hernández-Ramírez, Rodrigo; Ayanoglu, Hande
Type:
Year:
2024
Editor:
Grierson, Hilary; Bohemia, Erik; Buck, Lyndon
Author:
Series:
E&PDE
Institution:
IADE, Portugal; CODE, Berlin; The University of Sydney, Australia
Page(s):
258 - 263
DOI number:
ISBN:
978-1-912254-200
ISSN:
3005-4753
Abstract:
The speed and proficiency of generative Artificial Intelligence (AI) systems have proliferated in recent years, enabling more people, including design students, to use AI-generated images for their projects. However, it has been well documented that the Large Language Models supporting AI generators have incorporated troublesome gender and race biases during training (Wellner et al., 2020). Undergraduate students, whose visual culture and critical skills are still in development, often lack the capacity to identify such biases in the images they obtain when using AI generators. This can lead to visual outputs that perpetuate prejudiced representations of people (Hall et al., 2023). To better understand the nature of this problem and potential ways to mitigate it, we conducted a design probe study on a group of first-semester undergraduate design students in Lisbon, Portugal. The results of this study can be used by teachers to guide their students better and researchers to develop methodologies to help younger generations identify biases in AI generative systems. The impact of this research extends beyond the classroom and can benefit other educators and designers of future AI generative systems. Most importantly, it can contribute to curtailing the perpetuation of race and gender biases in today's society.
Keywords:
