Call for Papers

Algorithms have become integral to organizations and organizing. While tech giants have developed their business models on the algorithmic sorting and computation of data, government agencies, NGOs, and other organizations are also increasingly employing algorithms to target and shape behaviors across a range of organizational settings. Algorithms thus increasingly feature as powerful organizing forces. Yet, algorithms are also subject to new forms of organizing and management. Organizational practices, ranging from ‘readying’ data for algorithms to the situated (non-)use of algorithms, organize the power and agency of algorithmic technologies (Andrejevic et al., 2020; Plesner & Justesen, 2021; Ratner & Ruppert, 2019). In this sense, algorithms are both organizing and organized.
 
In this sub-theme, we are interested in the specific ways in which algorithms and organization co-constitute each other, asking how algorithms shape organizational worlds but also how organizational practices and negotiations shape the role algorithms can play in organizations. This calls for a situated and sociotechnical understanding of organization, focusing not only on the organizing aspects of algorithms and datafication but also dis-organizing and unanticipated effects (Ratner & Plotnikof, 2022; Seaver, 2017). Building on traditions that examine organization as a hybrid achievement with heterogeneous agencies (Dahlman et al., 2021; Latour, 1991), we invite explorations of how humans and algorithms are relationally configured in joint agencies (Suchman, 2012). Here, an equal focus on human and technological agencies, and how they are figured together, facilitates investigations of the development and use of algorithms in various organizational contexts, e.g., health care, music, or finance (Beunza & Millo, 2015; Amelang & Bauer, 2019; Heimstädt et al., 2021; Alaimo & Kallinikos, 2021).
 
Examining hybrid and heterogeneous organizational agencies in a context of datafication and pervasive algorithmic ordering involves detailed accounts of the conditions of possibility produced by algorithmic reason (Aradau & Blanke, 2022). These conditions include, but are not limited to, the new types of sequenced justification that form the technical backbone of algorithmic decision-support tools (Dencik & Stevens, 2021). Another key question for organizational scholars is how algorithmic technologies change organizational temporalities of anticipation and assessment, with machine learning modelling of big data rendering uncertain futures manageable in novel ways (Amoore, 2013; Cevolini & Esposito, 2020; Kellogg et al., 2020). Similarly, we should consider the spatial effects of algorithms and data, exploring to what extent organization is ‘becoming topological’ in a ‘new order of spatio-temporal continuity’ enabled by algorithmic data processing (Lury et al., 2012).
 
These algorithmic crossings in space and time re-actualize classical organizational questions of sociotechnical interrelations for the age of (semi-)autonomous machines (Leonardi, 2012; Fleming, 2019). As Bader and Kaiser (2019) show, while humans are detached from algorithmic decision-making in significant ways, they also remain centrally involved in it. Therefore, “human-algorithmic interaction is more important in understanding the implications of algorithms at work than are algorithms on their own” (Bader & Kaiser, 2019: 667). Whether beginning from the side of algorithms or from that of organizations, the central concern is relational, what is the agential space for organizing that emerges in the in-between of humans and technology?
 
Explorations of these issues might include but are not limited to:

• The co-constitution of algorithm and organization/organizing in processes of developing and using algorithmic technologies

• The role (s) of data collection and data management in algorithmic organizing and, conversely, new modes of datafication and algorithmic computation of organizations

• Emerging algorithmic temporalities, e.g., relationships between (datafied) pasts and predicted futures or new modes of organizing around uncertain futures

• Emerging algorithmic spatialities, e.g., how organization is ‘scaled’ through an increased datafication or new topological dis/continuities generated through data

• Configurations of human and technological agency, i.e., how human and machine agencies are joined and separated or relational modes of ordering contained in technological ‘scripts’

• Questions of algorithmic power and organization, e.g., how algorithmic models generate new power relations in organization and how these co-exist with previous power relations, e.g., regarding bias, surveillance, authority.

• Algorithms and dis/organization, addressing the unanticipated and dis/ordering aspects of the encounter between organization and algorithmic technologies

References

• Alaimo, C. & Kallinikos, J. (2021): Managing by data: Algorithmic categories and organizing. Organization Studies, 42 (9): 1385-1407.
• Amelang, K. & Bauer, S. (2019): Following the algorithm: How epidemiological risk-scores do accountability. Social Studies of Science, 49 (4): 476-502.
• Amoore, L. (2013): The politics of possibility. Risk and security beyond probability. Duke University Press.
• Andrejevic, M., Dencik, L., & Treré, E. (2020): From pre-emption to slowness: Assessing the contrasting temporalities of data-driven predictive policing. New Media & Society, 22 (9): 1528-1544.
• Aradau, C. & Blanke, T. (2022): Algorithmic reason. The new government of self and other. Oxford University Press.
• Bader, V. & Kaiser, S. (2019): Algorithmic decision-making? The user inter-face and its role for human involvement in decisions supported by artificial intelligence. Organization, 26 (5): 655-672.
• Beunza, D. & Millo, Y. (2015): Blended automation: Integrating algorithms on the floor of the New York Stock Exchange. SRC Discussion Paper, 38: 1-49.
• Cevolini, A., & Esposito, E. (2020): From pool to profile: Social consequences of algorithmic prediction in insurance. Big Data & Society, 7 (2).
• Dahlman, S., Guldbrandsen, I. T. & Just S. N. (2021): Algorithms as organizational figuration: The sociotechnical arrangements of a fintech start-up. Big Data & Society, 8 (1).
• Dencik, L. & Stevens. S. (2021): Regimes of justification in the datafied workplace: The case of hiring. New Media & Society, 0 (0).
• Fleming, P. (2019): Robots and organization studies: Why robots might not want to steal your job. Organization Studies, 40 (1): 23-37.
• Heimstädt, M., Egbert, S., and Esposito, E. (2021): A pandemic of prediction: On the circulation of contagion models between public health and public safety. Sociologica, 14 (3): 1-24.
• 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.
• Latour, B. (1991): Technology is society made durable. In J. Law (Ed.), A sociology of Monsters. Essays on Power, technology and Domination: Vol. Sociological Review Monograph 38 (pp. 103–131): Routledge.
• Leonardi, P. M. (2012): Materiality, sociomateriality, and socio-technical systems: What do these terms mean? How are they different? Do we need them? In P. M. Leonardi, B. M. Nardi & J. Kallinikos (Eds.), Materiality and organizing. Social interaction in a technological world (pp. 25-48). Oxford University Press.
• Lury, C., Parisi, L., & Terranova, T. (2012): Introduction: The Becoming Topological of Culture. Theory, Culture & Society, 29 (4–5): 3-35.
• Plesner, U., & Justesen, L. (2021): Digitalize and deny: Pluralistic collective ignorance in an algorithmic profiling project. Ephemera, 0 (0).
• Ratner, H., & Plotnikof, M. (2022): Technology and Dis/Organization: Digital data infrastructures as partial connections. Organization Studies, 43 (7): 1049-1067.
• Ratner, H. & Ruppert, E. (2019): Producing and project data: Aesthetic practices of government data portals. Big Data & Society, 6 (2): 1-16.
• Seaver, N. (2017): Algorithms as culture: Some tactics for the ethnography of algorithmic systems. Big Data & Society, 4 (2): 1-12.
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