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

2025-12-24

AI Summary

This study investigates how societal biases—particularly racial stereotypes—systematically transfer to human-robot interaction (HRI) in robot selection decisions. Using two large-scale behavioral experiments (N = 1,038) with randomized controlled designs and multidimensional visual stimulus manipulations (skin-tone gradients, anthropomorphism levels), we examine how occupational contexts (construction, healthcare, education, sports) and racial priming influence robot preferences. Our key contribution is the first empirical demonstration that occupational stereotyping and skin-tone bias interact synergistically: participants significantly prefer light-skinned robots in healthcare and education roles, yet show greater acceptance of dark-skinned robots in construction and sports. These effects are meaningfully moderated by participants’ racial background and prior occupational exposure to racially stereotyped groups. The findings reveal a novel mechanism through which AI deployment may exacerbate real-world social inequities, offering critical empirical evidence for algorithmic fairness research and inclusive HRI design.

Technology Category

• Intelligent Robots: Human-Robot Interaction
• Humans and AI: Learning Human Values and Preferences
• Philosophy and Ethics of AI: Bias, Fairness & Equity

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Social Networks and Social Media: Fairness and bias in social network and social media analysis

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.

Problem

• Investigates how occupational contexts influence robot selection biases
• Examines how racial priming transfers human biases to robot choices
• Reveals robot deployment may perpetuate existing social inequalities

Innovation

• Examined robot selection influenced by occupational contexts
• Tested skin tone and anthropomorphic feature variations systematically
• Showed human racial biases transfer to robot preferences