AI 中文总结
研究图像生成AI系统多变体界面中人类选择是否存在集中趋势偏差，通过控制设计集方差的实验发现，高方差会增加对接近中心设计的选择，揭示了生成输出与人类选择结果多样性间的潜在矛盾。
AI 中文摘要
图像生成人工智能系统通过生成多个设计变体供用户评估和选择，日益支持创造性工作。在这种人机共创工作流程中，选择成为关键阶段，人类判断引导人工智能生成的可能性走向最终结果。虽然呈现多个备选方案旨在鼓励探索，但同时呈现多个选项可能会在人类决策中引入系统偏差。借鉴整体感知理论，我们研究这些界面是否会引发集中趋势偏差，即倾向于选择更接近设计集中心的选项。我们进行了一项对照实验，操纵设计集的方差（高与低），并测量参与者在审美偏好和代表性任务中的选择。结果表明，更高的方差会增加两个任务中接近中心设计的选择。这些发现表明，图像生成人工智能系统中的多变体界面可能会限制选择多样性，揭示了生成输出的多样性与人类选择结果的多样性之间的潜在矛盾。
英文摘要
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.
CommentsAccepted at the 2026 Human-AI Interaction and Experience Design (HAXD 2026) Conference
