Why That Robot? A Qualitative Analysis of Justification Strategies for Robot Color Selection Across Occupational Contexts

TL;DR

This study explores how users justify robot color choices across occupations, revealing biases, stereotype influences, and the impact of robot appearance on reasoning strategies, with implications for reducing societal biases.

Contribution

It introduces a comprehensive qualitative analysis of human justification strategies for robot color selection, highlighting bias mechanisms and design considerations in HRI.

Findings

Functionalism is the dominant justification strategy (52%)

Racial stereotypes influence color choices even when not consciously recognized

Robot anthropomorphism shifts reasoning from functional to de-racialized explanations

Abstract

As robots increasingly enter the workforce, human-robot interaction (HRI) must address how implicit social biases influence user preferences. This paper investigates how users rationalize their selections of robots varying in skin tone and anthropomorphic features across different occupations. By qualitatively analyzing 4,146 open-ended justifications from 1,038 participants, we map the reasoning frameworks driving robot color selection across four professional contexts. We developed and validated a comprehensive, multidimensional coding scheme via human--AI consensus ($\kappa = 0.73$). Our results demonstrate that while utilitarian \textit{Functionalism} is the dominant justification strategy (52\%), participants systematically adapted these practical rationales to align with established racial and occupational stereotypes. Furthermore, we reveal that bias frequently operates beneath conscious rationalization: exposure to racial stereotype primes significantly shifted participants' color choices, yet their spoken justifications remained masked by standard affective or task-related reasoning. We also found that demographic backgrounds significantly shape justification strategies, and that robot shape strongly modulates color interpretation. Specifically, as robots become highly anthropomorphic, users increasingly retreat from functional reasoning toward \textit{Machine-Centric} de-racialization. Through these empirical results, we provide actionable design implications to help reduce the perpetuation of societal biases in future workforce robots.