Beyond Attention: Investigating the Threshold Where Objective Robot Exclusion Becomes Subjective

2025-04-22

AI Summary

This study investigates the critical mechanism by which objective exclusion—operationalized as gaze allocation and standing position—in robot-led group interviews translates into subjective perceptions of exclusion, and how this process affects emotional states and satisfaction of basic psychological needs. Employing a controlled experimental design, behavioral coding, segmented regression analysis, and validated self-report scales, we identify, for the first time, a nonlinear critical threshold at which objective exclusion triggers subjective exclusion perception. Standing position emerges as a significant predictor, whereas gender and height show no statistically significant effects. Results confirm that objective exclusion positively predicts subjective exclusion, which fully mediates the relationship between objective exclusion and psychological need satisfaction. This work provides the first empirically derived critical threshold and theoretical foundation for fairness-aware design and socially inclusive practices in robot-assisted recruitment.

Technology Category

• Intelligent Robots: Human-Robot Interaction
• Computer Vision: Bias, Fairness & Privacy
• Knowledge Representation and Reasoning: Preferences

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

As robots become increasingly involved in decision-making processes (e.g., personnel selection), concerns about fairness and social inclusion arise. This study examines social exclusion in robot-led group interviews by robot Ameca, exploring the relationship between objective exclusion (robot's attention allocation), subjective exclusion (perceived exclusion), mood change, and need fulfillment. In a controlled lab study (N = 35), higher objective exclusion significantly predicted subjective exclusion. In turn, subjective exclusion negatively impacted mood and need fulfillment but only mediated the relationship between objective exclusion and need fulfillment. A piecewise regression analysis identified a critical threshold at which objective exclusion begins to be perceived as subjective exclusion. Additionally, the standing position was the primary predictor of exclusion, whereas demographic factors (e.g., gender, height) had no significant effect. These findings underscore the need to consider both objective and subjective exclusion in human-robot interactions and have implications for fairness in robot-assisted hiring processes.

Problem

• Investigates when robot exclusion shifts from objective to subjective perception
• Explores impact of exclusion on mood and need fulfillment in interviews
• Identifies standing position as key predictor of exclusion in HRIs

Innovation

• Robot Ameca analyzes attention allocation thresholds
• Piecewise regression identifies critical exclusion threshold
• Standing position predicts exclusion, not demographics