TY - GEN
T1 - Characterizing the Flaws of Image-Based AI-Generated Content
AU - Vasir, Gursimran
AU - Huh-Yoo, Jina
N1 - Publisher Copyright:
© 2025 Copyright held by the owner/author(s).
PY - 2025/4/26
Y1 - 2025/4/26
N2 - The advancement of foundation models provides opportunities to efficiently generate multimodal content with reduced manual labor and time. Such AI-generated content (AIGC), however, can often be inaccurate, misleading, surreal, or hallucinating. Researchers developed multiple ways to systemize our understanding of the bias, errors, and failures of AI systems. However, little work has been done to characterize the flaws of image-based AIGC. In this work-in-progress, we analyzed 482 Reddit posts on various flaws of AIGC experienced by AIGC tool users. We found four themes describing the flaws of AIGC—logical fallacy, AI surrealism, misinformation, and cultural bias. We compare the results with the existing text-based AIGC framework on errors to discover unique flaws that image-based AIGC creates. We discuss implications toward a framework to describe, understand, and interpret flaws in AIGC in the broader context of understanding our social-technical world.
AB - The advancement of foundation models provides opportunities to efficiently generate multimodal content with reduced manual labor and time. Such AI-generated content (AIGC), however, can often be inaccurate, misleading, surreal, or hallucinating. Researchers developed multiple ways to systemize our understanding of the bias, errors, and failures of AI systems. However, little work has been done to characterize the flaws of image-based AIGC. In this work-in-progress, we analyzed 482 Reddit posts on various flaws of AIGC experienced by AIGC tool users. We found four themes describing the flaws of AIGC—logical fallacy, AI surrealism, misinformation, and cultural bias. We compare the results with the existing text-based AIGC framework on errors to discover unique flaws that image-based AIGC creates. We discuss implications toward a framework to describe, understand, and interpret flaws in AIGC in the broader context of understanding our social-technical world.
KW - AI bias and fairness
KW - AI-generated content
KW - AI-generated images
KW - Artificial Intelligence
KW - Chatbot
KW - hallucination
KW - Human-AI Interaction
KW - large language models
UR - http://www.scopus.com/inward/record.url?scp=105005735950&partnerID=8YFLogxK
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U2 - 10.1145/3706599.3720004
DO - 10.1145/3706599.3720004
M3 - Conference contribution
AN - SCOPUS:105005735950
T3 - Conference on Human Factors in Computing Systems - Proceedings
BT - CHI EA 2025 - Extended Abstracts of the 2025 CHI Conference on Human Factors in Computing Systems
T2 - 2025 CHI Conference on Human Factors in Computing Systems, CHI EA 2025
Y2 - 26 April 2025 through 1 May 2025
ER -