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.

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Carnegie-Mellon University
United States
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