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

TL;DR

This study investigates how societal biases, including occupational stereotypes and racial priming, influence human choices in selecting robots across different professional contexts, revealing context-dependent preferences and bias transfer from humans to robots.

Contribution

It provides empirical evidence that occupational and racial biases affect robot selection, demonstrating transfer of human stereotypes to artificial agents in various professional scenarios.

Findings

Healthcare and educational contexts favor lighter-skinned robots.

Construction and athletic contexts show preference for darker-toned robots.

Racial background of participants influences robot choice patterns.

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.