Lucas Gale M, Becerik-Gerber Burcin, Roll Shawn C
USC Institute for Creative Technologies, University of Southern California, Los Angeles, CA, USA.
Sonny Astani Department of Civil and Environmental Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA.
Patterns (N Y). 2024 Aug 29;5(9):101045. doi: 10.1016/j.patter.2024.101045. eCollection 2024 Sep 13.
With the exponential rise in the prevalence of automation, trust in such technology has become more critical than ever before. Trust is confidence in a particular entity, especially in regard to the consequences they can have for the trustor, and calibrated trust is the extent to which the judgments of trust are accurate. The focus of this paper is to reevaluate the general understanding of calibrating trust in automation, update this understanding, and apply it to worker's trust in automation in the workplace. Seminal models of trust in automation were designed for automation that was already common in workforces, where the machine's "intelligence" (i.e., capacity for decision making, cognition, and/or understanding) was limited. Now, burgeoning automation with more human-like intelligence is intended to be more interactive with workers, serving in roles such as decision aid, assistant, or collaborative coworker. Thus, we revise "calibrated trust in automation" to include more intelligent automated systems.
随着自动化普及率呈指数级增长，对这类技术的信任比以往任何时候都更加关键。信任是对某个特定实体的信心，特别是考虑到该实体可能给信任者带来的后果，而校准信任是指信任判断准确的程度。本文的重点是重新评估对自动化校准信任的普遍理解，更新这一理解，并将其应用于职场中员工对自动化的信任。早期的自动化信任模型是为劳动力中已经普遍存在的自动化而设计的，在这些模型中，机器的“智能”（即决策、认知和/或理解能力）是有限的。如今，具有更类人智能的新兴自动化旨在与员工进行更多互动，担任决策辅助、助手或协作同事等角色。因此，我们对“自动化校准信任”进行修订，以纳入更智能的自动化系统。
Zotero 插件