Automation Error Bias, Trust, and Dependence Behaviors in a Simulated Drone Collision Avoidance Task
实验研究了不完美自动化决策辅助系统的错误偏差（漏报与误报）如何影响操作者的信任和依赖行为（服从与信赖），发现错误偏差系统性地决定了依赖模式。
ObjectiveThis experiment examined how error biases of an imperfect automated decision aid system impacted trust and dependency behaviors in a simulated drone collision avoidance task.BackgroundPrior work on human-automation interaction indicates asymmetrical effects of error biases, misses, and false alarms, on compliance and reliance. Yet, it is unclear whether the effect is due to unbalanced perceptual salience of the automation errors or their trust toward the automated system.MethodSixty-eight participants interacted with a drone monitoring task with the assistance of a collision avoidance aid that varied in error bias (i.e., miss-prone and false-alarm prone). Participants' automation trust ratings and dependency behaviors (i.e., compliance and reliance) were measured.ResultsWith error biases equally salient, participants showed a similar decrease in levels of trust along multiple factors of automation trust when interacting with unreliable automation aids. Compliance rates were higher when interacting with a miss-prone system than a false-alarm prone system, whereas reliance rates showed the opposite pattern.ConclusionError bias determines compliance and reliance behaviors systematically. Saliency-matched false alarm and miss errors by automation degrade trust, potentially undermining the development of performance-based trust.ApplicationDesigners of automated systems should consider how different error types systematically affect dependency behaviors to create transparent systems that properly calibrate trust to the capability of the automation.