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.
|Original language||English (US)|
|Journal||International Journal of Human-Computer Interaction|
|State||Published - Oct 1 2020|