Algorithms at Work: The New Contested Terrain of Control

Kellogg, Katherine C.; Valentine, Melissa; Christin, Angèle

doi:10.5465/annals.2018.0174

Cited by 811 publications

(918 citation statements)

References 207 publications

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“…Algorithmic management of workers is drawing considerable attention in recent years (Burrell, 2016;Danaher et al, 2017;D'Cruz and Noronha, 2006;Dourish, 2016;Introna, 2016;Just and Latzer, 2017;Kellogg et al, 2020;Wood et al, 2019;Zarsky, 2016;Ziewitz, 2016;Zuboff, 2015Zuboff, , 2019. Algorithmic management or algorithmic governance refers to the use of computerized technologies to (partially) automate processes of decision-making and control, enabled through the unprecedented speed, scale and ubiquity of surveillance technologies, data processing as well as machine learning (based primarily on: Danaher et al, 2017;Helles and Flyverbom, 2019;Just and Latzer, 2017;Kellogg et al, 2020). In algorithmic management, decision-making and control may be exerted entirely through computerized systems (humans out of the loop), it may be subjected to human oversight (humans on the loop) or it may be used as a means to support human decisionmaking and control (humans in the loop) (Danaher, 2016).…”

Section: Algorithmic Management Empowers and Constrains Workersmentioning

confidence: 99%

Pacifying the algorithm – Anticipatory compliance in the face of algorithmic management in the gig economy

2020

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Algorithmic management is used to govern digital work platforms such as Upwork or Fiverr. However, algorithmic decision-making is often non-transparent and rapidly evolving, forcing workers to constantly adapt their behavior. Extant research focuses on how workers experience algorithmic management, while often disregarding the agency that workers exert in dealing with algorithmic management. Following a sociomateriality perspective, we investigate the practices that workers develop to comply with (assumed) mechanisms of algorithmic management on digital work platforms. Based on a systematic content analysis of 12,294 scraped comments from an online community of digital freelancers, we show how workers adopt direct and indirect “anticipatory compliance practices”, such as undervaluing their own work, staying under the radar, curtailing their outreach to clients and keeping emotions in check, in order to ensure their continued participation on the platform, which takes on the role of a shadow employer. Our study contributes to research on algorithmic management by (1) showing how workers adopt practices aimed at “pacifying” the platform algorithm; (2) outlining how workers engage in extra work; (3) showing how workers co-construct the power of algorithms through their anticipatory compliance practices.

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Section: Algorithmic Management Empowers and Constrains Workersmentioning

confidence: 99%

Pacifying the algorithm – Anticipatory compliance in the face of algorithmic management in the gig economy

2020

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“…In the following sections, we first present both our definitions and starting points for understanding AI and artificially intelligent agency, and propose that organisational scholars, while increasingly attentive to artificial intelligence, robots and algorithms generally (Baum & Haveman, 2020;Benaich & Hogarth, 2019;Fleming, 2019;Flyverbom, 2019;Kellogg et al, 2020), need to further consider the theoretical challenges and implications of AI agency and its near-future manifestations. To support this argument, we begin by bringing to bear sociomateriality as a theoretical lens on AI agency, followed by considerations of actor-network theory, institutional theory and the behavioural theory of the firm to further demonstrate how this step-change in AI agency challenges many of our theoretical lenses.…”

Section: Introductionmentioning

confidence: 99%

Revising the ‘science of the organisation’: theorising AI agency and actorhood

Rijmenam

Logue

2020

Innovation

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Artificial intelligence is a central technology underpinning the fourth industrial revolution, driving dramatic changes in contemporary cyber-physical systems and challenging existing ways of theorising organisations and management. AI agency and the rise of the artificially intelligent agent are both fundamentally different and yet increasingly similar to human agency in terms of intentionality and reflexivity. As 'Child AI' emerges-AI that is created by other AI-the early human design and interaction becomes increasingly distant and removed. These developments, while seemingly futuristic, change the human-technology interface through which we organise. In this essay, we explore understandings of AI agency, capability, and governance, and present implications for organisatonal theorising in sociomateriality, actor-network theory, institutional theory and the behavioral theory of the firm. We contribute to a growing and reflexive research agenda that can accommodate and regenerate theorising around this significant technological advancement.

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“…Two streams of research examine control in the platform economy. The first approach originates in the study of algorithmic control of the workplace (Aleks et al ., 2020; Kellogg et al ., 2019) and examines individual control mechanisms, such as performance management systems or task allocation algorithms (Lee et al ., 2015; Möhlmann and Zalmanson, 2017; Rosenblat et al ., 2017; Wood, 2018; Wood, 2019; Wood et al ., 2018). To date, the most explored platform control mechanism is the peer evaluation system (DeVault et al ., 2019; Gerber and Krzywdzinski, 2019; Maffie, forthcoming; Rosenblat, 2018; Rosenblat et al ., 2017; Rosenblat and Stark, 2016; Ticona et al ., 2018).…”

Section: Lack Of Conceptual Clarity In Platform Controlmentioning

confidence: 99%

Are we ‘sharing’ or ‘gig‐ing’? A classification system for online platforms

Maffie

2020

Industrial Relations Journal

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There is a growing debate about the nature and extent of platform control over workers. Companies claim they are merely ‘matchmakers’ while labour advocates argue that these organisations exercise granular control over workers. Blending the fields of information science and labour relations, this article develops a classification system of sequential control to delineate between ‘high control’ and ‘low control’ platforms. In doing so, this article provides a theoretical method to distinguish ‘sharing’ versus ‘gig’ platforms and argues that worker autonomy—how platforms foreclose worker choice—can be used to understand platform control.

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Algorithms at Work: The New Contested Terrain of Control

Cited by 811 publications

References 207 publications

Pacifying the algorithm – Anticipatory compliance in the face of algorithmic management in the gig economy

Pacifying the algorithm – Anticipatory compliance in the face of algorithmic management in the gig economy

Revising the ‘science of the organisation’: theorising AI agency and actorhood

Are we ‘sharing’ or ‘gig‐ing’? A classification system for online platforms

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