ABSTRACT

Accountability pressures have been found to increase worker engagement and reduce adverse biases in people interacting with automated technology, but it is unclear if these effects can be observed in a more laterally controlled human-AI task. To address this question, 40 participants were asked to coordinate with an AI agent on a resource-management task, with half of the participants expecting to justify their decision strategy, which comprised our accountability condition. We then considered the effects of accountability on performance, as measured by participants’ resource sharing behaviors, their individual, and joint task scores (throughput), and their perceived workload. Participants in the accountability group shared more resources with their AI partner, took more time to make decisions, and performed worse in the task individually, but had AI partners who performed better. We found no difference between groups on how prepared they felt they were to justify their decisions, and participants reported similar levels of workload. Results suggest accountability pressures can influence exchange strategies in human-AI tasks with lateral control.

摘要

已经发现问责制压力可以增加工作人员的敬业度，并减少与自动化技术进行交互的人们的不利偏见，但是目前尚不清楚是否可以在更加横向控制的人工AI任务中观察到这些影响。为了解决这个问题，要求40名参与者在资源管理任务上与AI代理进行协调，其中一半参与者希望证明自己的决策策略合理，这包括我们的责任条件。然后，我们考虑了问责制对绩效的影响，这可以通过参与者的资源共享行为，他们的个人和共同任务分数（吞吐量）以及他们的感知工作量来衡量。问责制小组的参与者与AI合作伙伴共享了更多资源，花费了更多时间进行决策，并且各自在任务上的表现更差，但是有表现更好的AI合作伙伴。我们发现两组之间在他们准备如何证明自己的决定有多充分的差异上，参与者报告的工作量水平相似。结果表明，问责制压力可能会影响带有侧向控制的人工AI任务的交换策略。