Algorithms and the Social Organization of Work

Abstract

<p>This chapter argues that the proliferation of automated algorithms in the workplace raises questions as to how they might be used in service of the control of workers. In particular, scholars have noted machine learning algorithms as prompting a data-centric reorganization of the workplace and a <italic>quantification</italic> of the worker. The chapter then considers ethical issues implicated by three emergent algorithmic-driven work technologies: automated hiring platforms (AHPs), wearable workplace technologies, and customer relationship management (CRM). AHPs are “digital intermediaries that invite submission of data from one party through preset interfaces and structured protocols, process that data via proprietary algorithms, and deliver the sorted data to a second party.” The use of AHPs involves every stage of the hiring process, from the initial sourcing of candidates to the eventual selection of candidates from the applicant pool. Meanwhile, wearable workplace technologies exist in a variety of forms that vary in terms of design and use, from wristbands used to track employee location and productivity to exoskeletons used to assist employees performing strenuous labor. Finally, CRM is an approach to managing current and potential customer interaction and experience with a company using technology. CRM practices typically involve the use of customer data to develop customer insight to build customer relationships.</p>

Artificial Intelligence as a Factor for Technological Unemployment and Dehumanization of the Labor Market

“…More research is needed to understand how AI will impact human lives, but it is also acknowledged that there is little to be done to stop the development, as the race for automation of decisions and jobs is almost predetermined to occupy a prominent place. This recognition reflects what seems to be a widespread perception among AI developers that as new technologies become more complex and powerful, it becomes increasingly difficult for laypeople to understand exactly what these technologies do and how they function, beyond the direct repercussions that may affect them (Ajunwa & Schlund, 2020).…”

Moral Cultivation and the Quantified Self: Assessing the Self Understanding of Data Profiles Generated by AI with a Virtue Ethics Approach

“…A well-researched example of the intersection between social media data and AI algorithms to generate data profiles can be found in the workplace. Ajunwa and Schlund (2020) describe how machine learning algorithms have promoted the "quantification of the worker in a manner and to a degree, previously unseen in history" (p. 806). One example they point to is the increasing use of automated hiring platforms (AHPs).…”

Workers’ right to the subject: The social relations of data production

“…The owners of data are disproportionately managers, police, and policymakers. Research looking at the rise in technological practices of algorithmic management, worker control, discrimination, and surveillance, illuminates the risks workers face when data is used to make decisions about them (Berg et al, 2018;Aloisi and De Stefano, 2022;O'Neil, 2016;Ajunwa, 2019;Adams-Prassl, 2020;Köchling and Wehner, 2020). Research that identifies algorithms as being ascribed identity prescriptions (Amoore, 2020) and which finds algorithms to be discriminatory (Williams et al, 2018;Ajunwa, 2020;Köchling and Wehner, 2020), is now relatively well known.…”