Data and Algorithms in the Workplace: A Primer on New Technologies

Lisa Kresge


The COVID-19 pandemic has intensified an already urgent discussion about the future of work and the role technology will play in shaping that future. Media reports have highlighted new technologies designed to monitor workers’ health and health-related behavior, and widespread employer adoption of remote worker monitoring tools.[1] Other reports have focused on the potential for automation and long-term job displacement to accelerate as a result of the conditions created by the pandemic.[2] Although these reports feature new COVID-related technologies, many are simply the latest application of data-driven technologies developed over the past decade. This working paper explores existing and emerging data-driven technologies and their various applications in the workplace.

Researchers and worker advocates have begun to explore data-driven systems and their implications for workers and society. Their research reports provide an excellent foundation for understanding various trends involving data-driven technologies in the workplace.[3] For example, and The Century Foundation published an article highlighting the growing trend of datafication in the workplace.[4] Upturn published a report outlining different technologies used in the hiring process and their implications for gender and racial equity in employment.[5] And Data & Society has published several reports covering workplace monitoring and algorithmic management on labor platforms and in traditional employment relationships.[6] This important research has shed light on different aspects of worker data collection and data-driven systems and how they operate in the workplace.

Building on these foundational reports, the purpose of this paper is to provide worker organizations and policymakers with a framework for understanding the broad range of data collection strategies and algorithmic systems currently in use or being developed for the workplace. Specifically, the focus here is on understanding the technologies themselves, the context in which they evolved, how they operate, and their potential applications in the workplace. This paper draws on research and analysis focused on specific technologies and the technology industry. Sources include the industry and technology consulting press, technology vendor materials, patent applications, academic research, reports from social research and advocacy institutes, popular media coverage of technology systems and vendors, as well as participation in conferences and meetings focused on computer science, artificial intelligence technologies, and the “future of work” more broadly.

To be clear, this type of technology-focused research is only one component of what will be required to understand the full scope of technological change emerging today. A full research agenda will need to include the perspectives of workers as well as those of employers, and an analysis of the technology production supply chain — which firms are producing new technologies for a given industry, who is funding them, who decides what gets developed in the labs, and so forth. Most important, we urgently need more research on how new technologies affect workplaces and workers (especially in terms of race and gender equity), and the factors driving variation in firms’ decisions about technology adoption.

Before moving on, it is important to emphasize that this paper describes emerging technologies; as such it may to a certain extent reflect technology developers’ aspirational efforts to advance a product rather than technologies in widespread use among employers. Thus, inclusion of a particular technology or practice in this report is not an indication of its prevalence or likelihood of adoption. That said, some of the technologies discussed in this paper have been in use for some time and are currently widely deployed by employers.
Regardless of whether the technologies are in the early stages of development or widely deployed by employers, some of these technologies have elicited concern among various stakeholders. This points to the need to generate a clear framework for assessing and governing the use of proposed technologies. It also highlights the importance of expanding institutional capacity to shape the trajectory of technology innovation.

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[1] For example, see media coverage about a Microsoft and United Health screening app, see Finley, D. (2020, May 18). Microsoft and UnitedHealth Group are rolling out a coronavirus screening app to US employers. Business Insider. Retrieved from and Cooley, B. (2020, May 22). Microsoft and UnitedHealth Group launch workplace health app. CNet. Retrieved from Also, see media coverage about Amazon’s social distancing AI, see Simonite, T. (2020). Amazon touts AI for social distancing amid worker complaints. Wired. Retrieved from and Vincent, J. (2020, June 16). Amazon deploys AI ‘distance assistants’ to notify warehouse workers if they get too close. The Verge. Retrieved from For media coverage regarding widespread monitoring, see Allyn, B. (2020, May 13). Your Boss Is Watching You: Work-From-Home Boom Leads to More Surveillance. NPR. Retrieved from; Golden, J., & Chemi, E. (2020, May 13). Worker monitoring tools see surging growth as companies adjust to stay-at-home orders. CNBC. Retrieved from; and Chyi, N. (2020, May 12). The workplace-surveillance technology boom. Slate. Retrieved from Also see Public Citizen. (2020). Workplace privacy after Covid-19. Retrieved from and Rodriguez, K., & Windwehr, S. (2020, September 10). Workplace Surveillance in Times of Corona. Electronic Frontier Foundation. Retrieved from

[2] See Ding, L., & Molina, J. S. (2020). “Forced Automation” by COVID-19? Early Trends from Current Population Survey Data. Retrieved from; MIT Technology Review Insights. (2020). Covid-19 and the Workforce: Critical workers, productivity, and the future of AI. MIT Technology Review. Retrieved from; and Muro, M., Maxim, R., & Whiton, J. (2020). The robots are ready as the COVID-19 recession spreads. Retrieved from

[3] In addition to the reports listed below, see Rogers, B. (2019). Beyond Automation: The Law & Political Economy of Workplace Technological Change (Vol. June). Retrieved from; Levy, K., & Barocas, S. (2018). Refractive surveillance: Monitoring customers to manage workers. International Journal of Communication, 12, 1166–1188. Retrieved from; Sánchez-m=Monedero, J., & Dencik, L. (2019). The datafication of the workplace. Data Justice Project, Cardiff University, 1–46. Retrieved from; Christin, A. (2020). What Data Can Do: A Typology of Mechanisms. International Journal of Communication, 14(0), 20.; and Kellogg, K. C., Valentine, M. A., & Christin, A. (2020). Algorithms at work: The new contested terrain of control. Academy of Management Annals, 14(1), 366–410.

[4] Adler-Bell, S., & Miller, M. (2018). The Datafication of Employment. The Century Foundation. Retrieved from

[5] Bogen, M., & Rieke, A. (2018). Help wanted: An examination of hiring algorithms, equity, and bias. Washington D.C. Retrieved from

[6] Mateescu, A., & Nguyen, A. (2019). Explainer: Workplace Monitoring & Surveillance. Data & Society Research Institute. Retrieved from and Mateescu, A., & Nguyen, A. (2019). Explainer: Algorithmic Management in the Workplace. Data & Society Institute, (February), 1–15. Retrieved from