Weaving ML with Human Aesthetic Assessments to Augment Design Space Exploration

People’s semantic connection with product design is an important signal that drives purchase decisions and overall satisfaction. In the concept design phase, capturing and responding to this signal is an important part of a product designer’s job. Yet in automotive design, where online experimentation is not a viable option, this process is driven by speculation about consumers' aesthetic preferences, drawing from designers’ intuition, prior experience, and domain knowledge. Our goal is to reduce the psychological distance between designers and consumers in the automotive concept design process and address potential biases (e.g., design fixation) that could emerge from it. In this work, we developed a novel framework and system that combines machine learning, human aesthetic assessments, and interface design to support designers in organizing a large space of automotive wheel designs. We hope our demo can stimulate discussions around using this framework for professional product design practice.