Further Reading: Chapter 28 — Algorithmic Management

Foundational Academic Research

Rosenblat, Alex, and Luke Stark. "Algorithmic Labor and Information Asymmetries: A Case Study of Uber's Drivers." International Journal of Communication 10 (2016): 3758–3784. The foundational academic analysis of Uber's algorithmic management practices, based on extensive interviews with Uber drivers and analysis of the platform's information architecture. Rosenblat and Stark coin the term "information asymmetries" to describe how the platform shapes worker behavior through selective data disclosure and manufactured demand signals. Essential reading for the gig economy analysis in this chapter.

Lee, Min Kyung, Daniel Kusbit, Evan Metsky, and Laura Dabbish. "Working with Machines: The Impact of Algorithmic and Data-Driven Management on Human Workers." CHI Conference on Human Factors in Computing Systems, 2015. The study that coined the "Working with Machines" framing, based on interviews with Uber and Lyft drivers. Lee and colleagues identify the core features of algorithmic management — automated direction, evaluation, and feedback — and document how workers experience and navigate them. A technical and sociological analysis accessible to non-specialists.

Rosenblat, Alex. Uberland: How Algorithms Are Rewriting the Rules of Work. University of California Press, 2018. Book-length expansion of Rosenblat's research on Uber drivers, providing the most comprehensive account of how algorithmic management transforms the driver experience. Covers behavioral nudging, information asymmetries, rating systems, and worker resistance. Essential reading for any serious engagement with gig economy surveillance.

Journalism and Investigative Reporting

Scheiber, Noam. "How Uber Uses Psychological Tricks to Push Its Drivers' Buttons." New York Times, April 2, 2017. The primary source for Case Study 28-1, documenting Uber's behavioral psychology program for driver management. Based on interviews with former Uber executives and behavioral economists who worked with the company. Provides the most detailed public account of how gig platforms deliberately exploit cognitive biases.

Satariano, Adam, and Emma Bubola. "Amazon Fires Employees Using an Algorithm, and They Can't Even Appeal to a Human." New York Times, 2019. Reporting on Amazon's automated termination practices, including the case of the worker terminated while on medical leave. Documents the "black box" manager problem through specific worker cases and limited Amazon responses.

Sainato, Michael. "I'm Not a Robot: Amazon Workers Condemn Unsafe, Grueling Conditions at Warehouse." The Guardian, February 2020. Worker testimony from multiple Amazon fulfillment centers, including detailed descriptions of the rate system, bathroom break realities, and algorithmic management consequences. Provides primary source material for the Bessemer and warehouse management analysis.

Abruzzo, Jennifer. "General Counsel Memorandum 22-06: Electronic Monitoring and Algorithmic Management of Employees Interfering with the Exercise of Section 7 Rights." National Labor Relations Board, October 2022. Available at nlrb.gov. The NLRB General Counsel's framework for applying NLRA protections to algorithmic management contexts. Directly addresses how automated monitoring and management can constitute unfair labor practices when they chill organizing activity. Essential primary source for the legal analysis in this chapter.

Mateescu, Alexandra, and Aiha Nguyen. "Explainer: Algorithmic Management in the Workplace." Data & Society Research Institute, 2019. Available at datasociety.net. Concise policy explainer on how algorithmic management systems work and what policy frameworks could govern them. Accessible and comprehensive, designed for policymakers and advocates rather than specialists.

European Commission. "Proposal for a Directive of the European Parliament and of the Council on Improving Working Conditions in Platform Work." 2021. Available at ec.europa.eu. The original Commission proposal for what became the EU Platform Work Directive, including the detailed rationale for employment presumption and algorithmic transparency requirements. Reading the legislative history reveals the specific worker harms that motivated each provision.

Books for Deeper Engagement

Dyer-Witheford, Nick, Atle Mikkola Kjøsen, and James Steinhoff. Inhuman Power: Artificial Intelligence and the Future of Capitalism. Pluto Press, 2019. A critical theory analysis of artificial intelligence as a form of capital, examining how automated management systems extend capitalist control into new domains of human experience. More theoretical than the chapter's analytical approach, but provides essential context for understanding algorithmic management within the broader political economy of AI.

Standing, Guy. The Precariat: The New Dangerous Class. Bloomsbury Academic, 2011. Standing's influential analysis of precarious employment — economic insecurity, contingency, inadequate social protections — provides essential context for understanding gig economy workers' relationship to algorithmic management. The "precariat" as a social class emerges precisely at the intersection of labor market flexibility and algorithmic control.

Zuboff, Shoshana. The Age of Surveillance Capitalism. PublicAffairs, 2019. Zuboff's analysis of behavioral modification as the ultimate product of surveillance capitalism applies directly to the Uber nudging case. Her concept of "instrumentarian power" — power that shapes behavior through the accumulation and analysis of behavioral data — is the theoretical framework within which algorithmic management's behavioral dimensions should be understood.