RAPID: Collaborative Research: The Transformation of Essential Work: Managing the Introduction of AI in Response to COVID-19

在COVID-19大流行中，数百万被视为“关键工人”的人从事体力劳动，如分类、清洁、垃圾收集和回收。为了减轻与这项工作相关的风险，正在加速推动引入人工智能（AI），以保护公众和工作人员免受疾病传播。然而，数十年的人机交互和组织沟通研究表明，将新技术引入工作场所并非易事；相反，技术改变并取代了现有的工作实践。本研究项目调查了废物管理行业在部署人工智能技术应对COVID-19危机时产生的有益创新和责任。它制定了一套协调人力和人工智能的最佳做法，以应对大流行，改变工作的未来。最佳做法将作为指导，介绍如何将人工智能纳入关键经济机构，以减轻COVID-19对公共卫生、社会和经济的负面影响。该指南将通过开放获取工具包、系列研讨会、新闻稿和社交媒体定期传达给工人、行业领袖和公众。这将潜在地惠及雇用或服务数以千万计工人的关键行业，包括废工、航运、制造业、零售业和食品服务业。该项目将通过一项多地点人种志研究进行，研究两个美国废物管理组织如何就引入自动化技术进行谈判，以减轻与COVID-19大流行相关的风险。第一个项目涉及匹兹堡国际机场的自动“地板护理”机器人。第二个项目涉及在德克萨斯州奥斯汀的一个单流回收厂使用人工智能分类系统。通过对两个地点的研究，研究小组有望获得关于如何根据专业、区域和机构规范引入和调整自动化的比较见解。数据收集将包括人种学现场记录、采访记录和媒体材料。延伸技术扩散和无形劳动理论，研究团队将定性地分析技术传播过程，从工人在日常工作中不断变化的形态中进行谈判的行为和观点中获得见解。通过反身性备忘录和“持续比较”编码，这项研究将确定行为模式，并建立一套可转移的观察结果。预计这将产生(1)关于促进或阻碍应对危机的快速技术引入的因素的实证研究结果，以及对自动化技术工作所需的人力（例如，校准，故障排除和维护）的具体见解；(2)有助于对创新扩散和工作场所技术如何通过使用重新发明的核心理解的理论研究结果。(3)各种必要工作环节的设计建议。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Millions of people deemed “essential workers” in the COVID-19 pandemic perform manual labor, such as sorting, cleaning, garbage collection, and recycling. To mitigate risks associated with this work, there is an accelerated push to introduce artificial intelligence (AI) to safeguard the public and workers from disease transmission. Yet, decades of human-computer interaction and organizational communication research shows that the introduction of new technologies into workplaces is not an easy transition; instead technologies transform and displace existing work practices. This research project investigates both beneficial innovations and liabilities arising in waste management industries, as they deploy AI technologies in response to the COVID-19 crisis. It develops a set of best practices for the coordination of human labor and AI to address the pandemic, transforming the future of work. The best practices will be presented as guidance on how to incorporate AI into critical economic institutions to mitigate the negative effects of COVID-19 on public health, society, and the economy. This guidance will regularly be communicated to workers, industry leaders, and the public through an open access toolkit, a workshop series, press releases, and social media. This will potentially benefit essential industries that employ or serve tens of millions of workers, including waste labor, shipping, manufacturing, retail, and food service.This project will be conducted through a multi-site ethnographic study, examining how two American waste management organizations negotiate the introduction of automated technologies, in an effort to mitigate risks associated with the COVID-19 pandemic. The first involves automated “floor care” robots at Pittsburgh International Airport. The second involves AI sorting systems in a single stream recycling plant, in Austin, Texas. By studying two sites, the research team is expected to gain comparative insight into how automation is introduced and attuned, according to professional, regional, and institutional norms. Data collection will include ethnographic fieldnotes, interview transcripts, and media materials. Extending theories of technological diffusion and invisible labor, the research team will qualitatively analyze the technology dissemination process, drawing insights from the actions and perspectives of workers as they negotiate the changing shape of their daily work. Through reflexive memos and “constant comparative” coding, the research will identify patterns of action and build a set of transferable observations. This is expected to yield (1) empirical findings on factors that promote or hinder rapid technological introduction in response to crisis, with specific insights on the human labor required to make automated technologies work (e.g., calibration, troubleshooting, and maintenance), (2) theoretical findings that contribute core understandings of the diffusion of innovation and how workplace technologies are reinvented through use, and (3) design recommendations for a variety of essential work sectors.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.