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

RAPID：协作研究：基本工作的转变：管理人工智能的引入以应对 COVID-19

• 批准号: 2037348
• 负责人: Sarah Fox
• 金额: $ 12.16万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-15 至 2022-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2037348&HistoricalAwards=false
• 关键词: RAPID; Collaborative; Research; Transformation; Essential

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.

在COVID-19大流行中，数百万人被视为“必要工作者”，从事体力劳动，如分类、清洁、垃圾收集和回收。为了降低与这项工作相关的风险，人们正在加速推动引入人工智能（AI），以保护公众和工人免受疾病传播。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不是一个容易的过渡;相反，技术改变并取代了现有的工作实践。该研究项目调查了废物管理行业在应对COVID-19危机时部署人工智能技术所产生的有益创新和责任。它开发了一套最佳实践，用于协调人力和人工智能，以应对这一流行病，改变未来的工作。最佳实践将作为如何将人工智能纳入关键经济机构的指导，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。该项目将通过一项多地点的人种学研究进行，研究美国两家废物管理组织如何就引入自动化技术进行谈判，以努力减轻与COVID-19大流行相关的风险。第一个涉及匹兹堡国际机场的自动“地板护理”机器人。第二个涉及德克萨斯州奥斯汀一家单流回收工厂的人工智能分拣系统。通过研究两个地点，研究团队有望根据专业、地区和机构规范，比较了解自动化是如何引入和调整的。数据收集将包括人种学田野记录，访谈记录和媒体材料。研究团队将扩展技术扩散和无形劳动的理论，定性分析技术传播过程，从工人的行动和观点中汲取见解，因为他们谈判日常工作的变化形式。通过自反性备忘录和“恒定比较”编码，研究将识别行动模式，并建立一套可转移的观察结果。预计这将产生（1）关于促进或阻碍快速技术引进以应对危机的因素的实证研究结果，具体了解使自动化技术发挥作用所需的人力（例如，校准、故障排除和维护），（2）有助于对创新扩散以及如何通过使用重新改造工作场所技术的核心理解的理论发现，以及（3）针对各种基本工作部门的设计建议。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Tech labor: a new interactions forum

科技劳工：新的互动论坛

• DOI: 10.1145/3466994
• 发表时间: 2021
• 期刊: Interactions
• 作者: Avle, Seyram;Fox, Sarah
• 通讯作者: Fox, Sarah

Patchwork: The Hidden, Human Labor of AI Integration within Essential Work

• DOI: 10.1145/3579514
• 发表时间: 2023-04
• 期刊: Proceedings of the ACM on Human-Computer Interaction
• 作者: Sarah E. Fox;S. Shorey;Esther Y. Kang;Dominique A. Montiel Valle;Estefania Rodriguez

AI and essential labor: representing the invisible work of integration

人工智能与基本劳动：代表着无形工作的融合

• DOI: 10.1145/3495253
• 发表时间: 2021
• 期刊: The ACM Magazine for Students
• 作者: Spektor, Franchesca;Rodriguez, Estefania;Shorey, Samantha;Fox, Sarah
• 通讯作者: Fox, Sarah

Stories from the Frontline: Recuperating Essential Worker Accounts of AI Integration

• DOI: 10.1145/3532106.3533564
• 发表时间: 2022-06
• 期刊: Proceedings of the 2022 ACM Designing Interactive Systems Conference
• 作者: Esther Y. Kang;Sarah E. Fox

Discarded Labor:: Countervisualities for Representing AI Integration in Essential Work

被抛弃的劳动力：代表人工智能在基本工作中的整合的反视觉

• DOI: 10.1145/3461778.3462089
• 发表时间: 2021
• 期刊: DIS '21: Designing Interactive Systems Conference 2021
• 作者: Spektor, Franchesca;Rodriguez, Estefania;Shorey, Samantha;Fox, Sarah
• 通讯作者: Fox, Sarah