Deconstructing Taste: Toward a Human-Centered AI Framework for Modeling Consumer Aesthetic Perceptions

TL;DR

This paper introduces a human-centered AI framework that models consumer aesthetic perceptions by linking subjective taste evaluations with interpretable design features, enhancing design decision-making.

Contribution

It presents an integrated computational approach that combines human subjective evaluations with machine-extracted features to better understand consumer aesthetic preferences.

Findings

Framework links subjective taste with design features

Enhances interpretability of aesthetic assessments

Supports better design decision-making

Abstract

Understanding and modeling consumers' stylistic taste such as "sporty" is crucial for creating designs that truly connect with target audiences. However, capturing taste during the design process remains challenging because taste is abstract and subjective, and preference data alone provides limited guidance for concrete design decisions. This paper proposes an integrated human-centered computational framework that links subjective evaluations (e.g., perceived luxury of car wheels) with domain-specific features (e.g., spoke configuration) and computer vision-based measures (e.g., texture). By jointly modeling human-derived (consumer and designer) and machine-extracted features, our framework advances aesthetic assessment by explicitly linking model outcomes to interpretable design features. In particular, it demonstrates how perceptual features, domain-specific design patterns, and consumers' own interpretations of style contribute to aesthetic evaluations. This framework will enable product teams to better understand, communicate, and critique aesthetic decisions, supporting improved anticipation of consumer taste and more informed exploration of design alternatives at design time.