公共服务自动化的讽刺——班布里奇重访

Ida Lindgren
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摘要

机器人流程自动化(RPA)和类似的数字技术目前正用于公共服务提供工作流程的自动化。尽管RPA在数字政府环境中实现了新的经验现象,但工作自动化并不是一个新的、未被充分研究的现象。在本文中,我认为存在这样一种风险,即数字政府研究人员将这一现象视为全新的,忽略了多年来在其他相关研究领域取得的经验。为了防止这种风险,我呼吁数字政府研究人员向邻近的研究学科学习。我遵循这一呼吁,提出了自动化的四个讽刺,摘自Lisanne Bainbridge自1983年的标志性作品,并将它们与公共服务自动化背景联系起来,使用地方政府组织中的RPA示例。具有讽刺意味的是,在自动化系统和人机交互的设计中,关于人类和人类错误的潜在观点的矛盾,以及人机交互在新任务(监控和接管)方面的后果,责任的重新配置,以及与自动化系统交互的人类的分离和重新技能。这些发现可以指导数字政府的理论化和公共服务自动化的实证研究。借鉴相关领域的研究成果,有助于更好地理解和积累公共服务自动化的特点和影响方面的知识。从已建立的知识库中学习对于避免可预见的项目和实现失败也很重要。最后,本文提出了对公共服务自动化感兴趣的数字政府研究人员未来研究课题的建议。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Ironies of Public Service Automation – Bainbridge Revisited
Robotic Process Automation (RPA) and similar digital technologies are currently being implemented for automation of work processes in public service provision. Although RPA enables new empirical phenomena in the digital government context, automation of work is not a new and understudied phenomenon. In this paper, I claim that there is a risk that digital government researchers treat this phenomenon as being entirely new, omitting years of experiences made in other, related, fields of research. To prevent this risk, I call for digital government researchers to learn from adjacent research disciplines. I follow this call and present four ironies of automation, extracted from Lisanne Bainbridge's iconic work from 1983, and relate these to the public service automation context, using examples of RPA in local government organizations. The ironies concern contradictions in the underlying views on humans and human error in the design of automated systems and human-automation interaction, as well as the consequences of human-automation interaction in terms of new tasks (monitoring and take-over), re-configurations of responsibilities, and de- and re-skilling of humans interacting with automated systems. These findings can guide digital government theorization and empirical research on public service automation. Learning from adjacent fields of research is important to better understand and cumulatively build knowledge on the characteristics and effects of public service automation. Learning from an established knowledge base is also important to avoid foreseeable project and implementation failures. The paper is concluded with suggestions on future research topics for digital government researchers interested in public service automation.
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