Central Tendency Bias in Human Selection of AI-Generated Design Variations
2026-07-10 • Human-Computer Interaction
Human-Computer Interaction
AI summaryⓘ
The authors studied how people choose images when AI systems show many design options at once. They found that people tend to pick images that are closer to the middle of the group, especially when the images vary a lot. This means that even if AI creates diverse designs, people might still focus on a narrower set in their selections. The research highlights a challenge in using AI tools for creative decisions, where choices may become less varied than expected.
image-generation AIhuman-AI co-creationselection biascentral tendency biasensemble perception theorydesign varianceaesthetic preferencerepresentativeness task
Authors
Huiyang Chen, Keqing Jiao
Abstract
Image-generation AI systems increasingly support creative work by producing multiple design variations for users to evaluate and select. In such human-AI co-creation workflows, selection becomes a critical stage where human judgment guides AI-generated possibilities toward final outcomes. While presenting multiple alternatives is intended to encourage exploration, the simultaneous multi-option presentation may introduce systematic biases in human decision making. Drawing on ensemble perception theory, we investigate whether these interfaces induce central tendency bias-the tendency to favor options closer to the center of a design set. We conducted a controlled experiment manipulating the variance of design sets (high vs. low) and measured participants' selections in both aesthetic preference and representativeness tasks. Results show that higher variance increases the selection of center-proximal designs across both tasks. These findings suggest that multi-variation interfaces in image-generation AI systems may constrain selection diversity, revealing a potential tension between diversity in generated outputs and diversity in human selection outcomes.