Douer Nir, Meyer Joachim
26745 Tel Aviv University, Israel.
Hum Factors. 2022 Mar;64(2):359-371. doi: 10.1177/0018720820940516. Epub 2020 Aug 4.
We explore users' and observers' subjective assessments of human and automation capabilities and human causal responsibility for outcomes.
In intelligent systems and advanced automation, human responsibility for outcomes becomes equivocal, as do subjective perceptions of responsibility. In particular, actors who actively work with a system may perceive responsibility differently from observers.
In a laboratory experiment with pairs of participants, one participant (the "actor") performed a decision task, aided by an automated system, and the other (the "observer") passively observed the actor. We compared the perceptions of responsibility between the two roles when interacting with two systems with different capabilities.
Actors' behavior matched the theoretical predictions, and actors and observers assessed the system and human capabilities and the comparative human responsibility similarly. However, actors tended to relate adverse outcomes more to system characteristics than to their own limitations, whereas the observers insufficiently considered system capabilities when evaluating the actors' comparative responsibility.
When intelligent systems greatly exceed human capabilities, users may correctly feel they contribute little to system performance. They may interfere more than necessary, impairing the overall performance. Outside observers, such as managers, may overweigh users' contribution to outcomes, holding users responsible for adverse outcomes when they rightly trusted the system.
Presenting users of intelligent systems and others with performance measures and the comparative human responsibility may help them calibrate subjective assessments of performance, reducing users' and outside observers' biases and attribution errors.
我们探讨用户和观察者对人类与自动化能力的主观评估,以及人类对结果的因果责任。
在智能系统和先进自动化中,人类对结果的责任变得模糊不清,责任的主观认知也是如此。特别是,积极与系统合作的行为者可能与观察者对责任的认知不同。
在一项针对成对参与者的实验室实验中,一名参与者(“行为者”)在自动化系统的辅助下执行决策任务,另一名参与者(“观察者”)则被动观察行为者。我们比较了在与两个具有不同能力的系统交互时,这两个角色对责任的认知。
行为者的行为符合理论预测,行为者和观察者对系统及人类能力以及相对的人类责任的评估相似。然而,行为者往往将不良结果更多地归因于系统特征而非自身局限,而观察者在评估行为者的相对责任时对系统能力的考虑不足。
当智能系统大大超越人类能力时,用户可能会正确地觉得自己对系统性能贡献不大。他们可能会过度干预,从而损害整体性能。外部观察者,如管理者,可能会高估用户对结果的贡献,在他们正确信任系统时让用户对不良结果负责。
向智能系统用户及其他人员展示性能指标和相对的人类责任,可能有助于他们校准对性能的主观评估,减少用户和外部观察者的偏差及归因错误。