From Human Bias to Robot Choice: How Occupational Contexts and Racial Priming Shape Robot Selection

TL;DR

This work examines how occupational contexts and racial cues influence robot selection in HRI through two large experiments (N = 1,038) that manipulate robot color (skin tone) and human-likeness. It demonstrates context-dependent racial biases: lighter-skinned robots are preferred in healthcare and tutoring, while darker tones are more acceptable in construction and sports, with racial priming amplifying stereotype-consistent choices. Using multilevel logistic regression, the study shows substantial variance across participants and strong priming effects (OR ≈ 5.93) that transfer social stereotypes from humans to robots. These findings imply that biased deployment of robotic agents could reinforce social inequalities, motivating design interventions to reduce racial salience in robotic representations and to ensure fairer HRI outcomes.

Abstract

As artificial agents increasingly integrate into professional environments, fundamental questions have emerged about how societal biases influence human-robot selection decisions. We conducted two comprehensive experiments (N = 1,038) examining how occupational contexts and stereotype activation shape robotic agent choices across construction, healthcare, educational, and athletic domains. Participants made selections from artificial agents that varied systematically in skin tone and anthropomorphic characteristics. Our study revealed distinct context-dependent patterns. Healthcare and educational scenarios demonstrated strong favoritism toward lighter-skinned artificial agents, while construction and athletic contexts showed greater acceptance of darker-toned alternatives. Participant race was associated with systematic differences in selection patterns across professional domains. The second experiment demonstrated that exposure to human professionals from specific racial backgrounds systematically shifted later robotic agent preferences in stereotype-consistent directions. These findings show that occupational biases and color-based discrimination transfer directly from human-human to human-robot evaluation contexts. The results highlight mechanisms through which robotic deployment may unintentionally perpetuate existing social inequalities.

Figures (7)

• Figure 1: Experimental procedure, task scenarios, and robot stimuli. The robot stimuli vary by both colour and morphology, with morphology operationalized as five human-likeness levels. Appendix Figure \ref{['fig:robot_stimuli']} provides the complete stimulus set.
• Figure 2: Robot color selection across ethnic groups and tasks. The two with hatch pattern are the two non-skin tones.
• Figure 3: Robot color preferences by human-likeness level across tasks. Dashed lines show non-skin-tone color selection rates; solid lines show skin-tone color selection rates. Red curves display quadratic trend fits for each color category.
• Figure 4: Robot color selection by participant races and tasks. The red lines are the overall average.
• Figure 5: Robot color selection patterns under racial priming across tasks. Compares Prime A (stereotype-congruent) versus Prime B (stereotype-incongruent) conditions across construction, hospital, tutoring, and sports contexts. Red lines show baseline selection rates without priming.