Algorithms on the Shop Floor

Data-Driven Technologies in Organizational Context

June 14, 2019

Hosted by Data & Society Research Lead Madeleine Clare Elish and Research Analyst Elizabeth Anne Watkins

There is an increasing need to understand how automated, algorithmic, AI, or otherwise data-driven technologies are being integrated into organizational contexts and processes of all sizes and types. How can we make sense of what gets lost, what gets gained, and what gets changed? Many of these questions are long standing themes in organizational studies and social science research that examines the social complexities of working on the machine shop floor. Still, what is unique to this moment and how do such integrations provoke new shifts in power relations and social values?

To explore these questions, Data & Society hosted an academic workshop on the intersection of technology and organizational theory and practice on June 14, 2019. Twelve papers were selected following a public call for participation. The papers examined a wide range of contexts, including health care, public social services, on-demand labor, journalism, international software development, law, and fin-tech. Participants included researchers and practitioners studying how new technologies are introduced, incorporated, resisted, or maintained within organized groups, and the changes this integration brings.

Workshop Program

These papers were shared as works in progress. Therefore, we provide only the titles and summaries of the drafts workshopped. Please be in touch with the authors to learn more about their work.

Paper: Becoming Data-Driven: Dashboards and Data Analytics in City Hall
Author: Burcu Baykurt
Discussant: Julia Ticona

This paper examines how the adoption of data analytics transforms organizational practices in city hall, focusing in particular on the everyday work of producing the kind of knowledge that is deemed to have practical value in local governance. Drawing on three years of ethnographic fieldwork in Kansas City where the municipality and local entrepreneurs have joined Cisco to set up a pilot smart city program since 2015, I examine the ways public officials value and evaluate urban (big) data in order to reconcile the multiple goals they profess to accomplish: bridging divides across the city, generating innovation and economic growth, identifying novel insights about the city, and creating efficiencies. Through a particular case study of searching for “an algorithmic model that works for the city,” I argue that, rather than solely seeking to rationalize local governance, data analysts and public officials move back and forth between multiple types of worth and evaluation as they incorporate data analytics and urban dashboards into everyday decision-making. While these new modes of knowledge production restructure the ways public officials see the city, seeing like a city also shapes the possibilities and limits of governing by data.

Paper: Making out While Driving: Control and Autonomy in the Algorithmic Workplace
Author: Lindsey Cameron
Discussant: Mary Beth Watson-Manheim

Increasingly, algorithms are changing how work is structured and navigated. Drawing on a 26-month ethnography of the ride hailing industry, the largest sector of the on-demand economy, this paper describes how the shift from human to algorithmic managers affects the nature of managerial control and worker autonomy.

More information available at

Paper: The Technopolitics of Data-Driven Society: The Difference Organizationally Embedded Users Make with Data-Driven Technologies
Author: Taylor Cruz
Discussant: Danya Glabau

Data analytics and information technology have unleashed an unparalleled data revolution, transforming social institutions ranging from health care, education, and criminal justice. Previous social science scholarship has characterized the political nature of this sociotechnical change within two scholarly traditions: Governmentality scholars emphasize the use of data in the management and conduct of life itself, while others consider the sociocultural values embedded within technical design. In this paper, I present a third approach in studying the politics of the sociotechnical by considering organizationally embedded users and their everyday engagements with technology.

Paper: AI, Lawyers, and Professional Work: The Practice of Law with Automated Decision Support Technologies
Authors: Daniel L. Kluttz and Deirdre K. Mulligan
Discussant: Anne-Laure Fayard

Technical systems employing algorithms are shaping and displacing human decision-making in a variety of fields. As technology reconfigures work practices, researchers have documented potential loss of human agency and skill, confusion about responsibility, diminished accountability, and both over-and under-reliance on decision-support systems. The objective of our ongoing empirical study is to analyze the organizational structures, professional rules and norms, and technical system properties that shape professionals’ understanding and engagement with such systems in practice. As a case study, we examine decision-support systems marketed to legal professionals, focusing primarily on technologies marketed for e-discovery purposes.

Soon to be published in the Berkeley Technology Law Journal, pre-print available at

Paper: Data-Driven Duties in AI Development
Author: Frank Pasquale
Discussant: Emanuel (Manny) Moss

Corporations will increasingly attempt to substitute AI and robotics for human labor. This evolution will create novel situations for tort law to address. However, tort will only be one of several types of law at play in the deployment of AI. Regulators will try to forestall problems by setting standards, and corporate lawyers will attempt to deflect liability via contractual disclaimers and exculpatory clauses. The interplay of tort, contract, and regulation will not just allocate responsibility ex post, spreading the costs of accidents among those developing and deploying AI, their insurers, and those they harm. This matrix of legal rules will also deeply influence the development of AI, including the industrial organization of firms and capital’s and labor’s relative share of productivity and knowledge gains.

Paper: The Autonomy Paradox
Author: Caitlin Petre
Discussant: Matt Rafalow

“The Autonomy Paradox” is the final empirical chapter of my book-in-progress, tentatively titled Desperate Measures: Web Metrics, Journalism, and the Future of Knowledge Work in the Digital Age (under advanced contract with Princeton University Press). The book draws on fieldwork and interviews at Chartbeat (a prominent data analytics company), the New York Times, and Gawker Media to explore the growing role of audience metrics in the contemporary journalism field.

Paper: Keeping Documents In: From Social Surveillance to Digital Suspicion in South Korean Offices
Author: Michael Prentice
Discussant: Paul Dourish

Visitors to corporate office buildings in Seoul can expect rigorous security checks involving RFID-enabled ID badges, stickers over cameras, metal detectors, and confiscation of electronic storage devices. The same measures are also extended to employees who operate in highly securitized office environments. Reading these security technologies along other forms of social surveillance, this paper looks at shifts from cultures of protection to cultures of suspicion in Korea’s corporate world. Based on fieldwork at a Korean conglomerate in Seoul and interviews with corporate employees between 2013-2015, 2017, and 2018, I describe how managers at the Sangdo Group perceived a shift from a culture of mutual and normative protection to a culture of criminal suspicion, prompted by the introduction of mandatory permission-based cloud storage.

Paper: Working Algorithms: Software Automation and the Future of Work
Author: Benjamin Shestakofsky
Discussant: Ruthanne Huising

This is a chapter from a forthcoming book project. I draw on 19 months of participant-observation research at a software startup I call AllDone to examine the inner workings of an online market for local services. The book project contributes to debates about the future of work by investigating what drives the dynamism of gig economy platforms and revealing the hidden labor involved in enabling and managing rapid technological change.

Paper: Quantifying “Self-Sufficiency”: From an Interactional to a Technocratic Approach to Nonprofit Anti-Poverty Work
Author: Mélanie Terrasse
Discussant: Kadija Ferryman

In human services, growing accountability and evaluation pressures have contributed to the growing use of information technology (IT) to rationalize, standardize, and routinize service delivery. Drawing from a 9-month ethnography of the implementation of a shared client database across a network of anti-poverty nonprofits, this article examines the development of a computerized needs assessment to quantify client “self-sufficiency.” As a central feature of the database initiative, the “self-sufficiency matrix” evolved from a flexible, paper-based goal-planning tool to a standardized, electronic assessment to reliably measure client outcomes. I find that the development of this new tool exemplified a larger tension in social services between administrative and frontline approaches to social services.

Paper: The Paradox of Precarity: Transnational Work in Retail Logistics
Author: Nantina Vgontzas
Discussant: Anita Say Chan

Drawing on observations and interviews, the analysis compares two fulfillment networks and examines how algorithms effect the labor process in ecommerce warehouses. A version of the paper will appear as a chapter in a forthcoming book co-edited by Jake Wilson and Ellen Reese called Amazon Capitalism: Resisting the World’s Most Powerful Corporation.

Paper: “Small Steps Lead to a Faster Stride”: Cultures of Rapid Iteration in China’s Fin-Tech Development
Author: Jing Wang
Discussant: danah boyd

The financial application of digital technologies (or so-called fin-techs) has drawn global attention from investors, researchers, and financial practitioners. Fin-tech apps such as Venmo, Robinhood, and Stash have covered payment, loans, and investment services, all fundamental for peoples’ everyday economic life. Although fin-techs were invented in the U.S., today’s global fin-tech innovations have been led by Chinese companies such as Ant Financial and Tencent Finance. Based on ethnographic work in Shanghai and Hangzhou in 2017, this paper examines the competence of Chinese fin-tech companies with a focus on how the affordances of digital technologies have been integrated with organizational structures and workflows.

Paper: Failing the Metric But Saving Lives: The Case of Sepsis Treatment
Author: Rosalie Winslow
Discussant: Dan Bouk

Quality metrics in the health care sector have become a key component of ensuring improved health outcomes and care equity. With the emergence of electronic health records and widespread use of information technology in healthcare, measuring health care processes and outcomes as a form of accountability has become the primary method for achieving quality. However, definitions of quality and methods of achieving high quality care differs widely between organizations, and what these metrics mean for clinicians and patients remains unclear. This paper seeks to understand how quality measurement restructures the organization and delivery of care and to determine how it impacts care equity.

Workshop Participants

  • Sanna Ali, Stanford University
  • Tamar Ashuri, Tel Aviv University
  • Joshua Barbour, University of Texas at Austin
  • Burcu Baykurt, University of Massachusetts, Amherst
  • Dan Bouk, Data & Society
  • danah boyd, Data & Society/Microsoft Research
  • Stefanie Büchner, Leibniz University Hannover, Germany/Visiting Research at UC Irvine
  • Lindsey Cameron, University of Michigan/(Incoming) University of Pennsylvania
  • Taylor Cruz, California State University, Fullerton
  • Patrick Davison, Data & Society
  • Fernando Delgado, Cornell University
  • Paul Dourish, School of Information and Computer Sciences, University of California, Irvine
  • Madeleine Clare Elish, Data & Society
  • Diana Enriquez Princeton University
  • Anne-Laure Fayard, New York University, School of Engineering
  • Kadija Ferryman, Data & Society/NYU Tandon
  • Sophia Fu, Rutgers University
  • Michele Gilman, University of Baltimore
  • Danya Glabau, Implosion Labs
  • Elizabeth Hansen, Shorenstein Center on Media, Politics, Public Policy, Harvard Kennedy School
  • Ruthanne Huising, EMLyon Business School
  • Anne Jonas, UC Berkeley School of Information
  • Elizabeth Kaziunas, AI Now Institute, NYU
  • Vera Khovanskaya, Cornell University
  • Mark Latonero, Data & Society
  • Sarah Lebovitz, New York University
  • Kevin Lee, New York University
  • Ethel Mickey, Wellesley College
  • Emanuel (Manny) Moss, CUNY Graduate Center/Data & Society
  • Deirdre Mulligan, School of Information, UC Berkeley
  • Zanele Munyikwa, MIT Sloan School of Management
  • Liz Noll, Independent
  • Firaz Peer, Georgia Institute of Technology
  • Caitlin Petre, Rutgers University
  • Casey Pierce, University of Michigan School of Information
  • MichaelPrentice, Brandeis University/(incoming) University of Manchester Digital Futures Programme
  • Rida Qadri, Massachusetts Institute of Technology
  • Matt Rafalow, YouTube
  • Robert Rapoport, Digital Culture Research Lab, Leuphana University Luneburg
  • David Robinson, Upturn/Cornell University
  • Sarah Sachs, Columbia University
  • Laura Sartori, Universita di Bologna
  • Anita Say Chan, iSchool at Illinois
  • Benjamin Shestakofsky, University of Pennsylvania
  • Mélanie Terrasse, Princeton University
  • Dory Thrasher, New York City Department of Social Services
  • Julia Ticona, University of Pennsylvania
  • Amos Toh, Human Rights Watch
  • Nantina Vgontzas, New York University
  • Jing Wang, Tulane University
  • Elizabeth Anne Watkins, Data & Society
  • Mary Beth Watson-Manheim, University of Illinois Chicago
  • Rosalie Winslow, University of California, San Francisco

Participants ranged in career stages and came from diverse disciplines studying technology in organizations. Focus areas included management, organization studies, communications, information studies, computer-supported cooperative work, computer-human interaction, science and technology studies, art, ethics, labor, law, policy, anthropology, and design research. As a result, attendees engaged with scholars and practitioners outside of their field of study, building a broader cross-disciplinary field, and strengthening both relationships and research through participation in the workshop.

The Structure: Academic Workshops at Data & Society

The purpose of the D&S academic workshop series is to enable deep dives with a broad community of interdisciplinary researchers into topics at the core of Data & Society’s concerns.

The structure of the D&S Workshop series is designed to maximize scholarly thinking about the evolving and societally important issues surrounding data-driven technologies. The workshop format is first and foremost an opportunity to collectively think and help construct a field. Everyone who attends is expected to be an active participant and contribute to rich conversations. Participants were asked to read three full papers in advance of the event and prepare comments for intensive discussion. Some participants were asked to be discussants of papers, where they led the conversation and engaged the room. Authors in attendance did not present their papers, but rather participated in critical discussion with the assembled group focusing on the explicit intent of making the work stronger and more interdisciplinary. We believe that it is through intense intellectual engagement with other scholars around research that new insights can emerge.

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