Further Reading — Chapter 16

Investigative Journalism

1. Cox, Joseph. "How Ring Transmits Fear to Millions and Shares Data with Police." Motherboard/Vice, 2019.

Cox's foundational reporting on Ring's law enforcement partnerships, based on contracts obtained through public records requests. This piece established the template for subsequent investigative coverage of Ring and revealed the promotional obligations police departments accepted as part of the partnership program. Essential reading for understanding the transactional structure of Ring's government relationships. Available through Vice's online archive.

2. Haskins, Caroline. "Amazon's Neighbors App Has Become a Powerful Surveillance System That Police Love — and Residents Have No Say In." BuzzFeed News, 2019.

An early and detailed investigation into how Ring's Neighbors app functioned as a de facto law enforcement tool, drawing on interviews with police liaisons, Ring users, and civil liberties advocates. Haskins documented the normalization of Neighbors-to-police reporting pipelines before Ring's later policy changes. BuzzFeed News archive.

3. Hill, Kashmir. "Amazon Knows What You Bought, and Now It Knows Where You Live." New York Times, 2021.

Hill's reporting on the integration of Ring's geographic data with Amazon's broader commercial data ecosystem. Examines the implications of a single company holding purchase history, voice recordings (Alexa), delivery data, and neighborhood camera maps. Accessible through NYT subscription or library access.

Academic Research

4. Brayne, Sarah. Predict and Surveil: Data, Discretion, and the Future of Policing. Oxford University Press, 2021.

An ethnographic study of predictive policing in the Los Angeles Police Department, with significant attention to how private data partnerships — including camera networks — integrate with official law enforcement analytics. While focused on LAPD rather than Ring specifically, Brayne's framework for understanding data-driven policing is directly applicable to Ring's law enforcement integration model. Highly accessible for undergraduate readers.

5. Selbst, Andrew D. "Disparate Impact in Big Data Policing." Georgia Law Review, 2017.

A legal analysis of how algorithmic and data-driven policing tools reproduce and amplify racial disparities from historical policing data. The theoretical framework Selbst develops — examining how neutral-seeming algorithmic tools encode biased assumptions — applies directly to Ring's Neighbors app and its racial profiling dynamics. Available through law review databases and SSRN.

6. Eubanks, Virginia. Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor. St. Martin's Press, 2018.

While focused primarily on automated decision-making in social services rather than consumer surveillance hardware, Eubanks's analysis of how technological systems reproduce and amplify existing social inequalities is essential for understanding Ring's racial dynamics. Her concept of the "digital poorhouse" — technology that targets and controls low-income and minority communities — illuminates the structural stakes of neighborhood surveillance networks.

Civil Liberties and Advocacy

7. Electronic Frontier Foundation. "Ring's Hidden Data Practices Revealed: EFF Analysis of Ring Privacy Policy." EFF.org, 2020.

A detailed technical and legal analysis of Ring's privacy policy, identifying provisions that allow data uses Ring has not prominently disclosed. The EFF's surveillance self-defense resources, updated annually, provide practical guidance for users seeking to limit Ring's data collection. Free and fully available at eff.org.

8. Upturn. "Making the Case: Ring's Law Enforcement Partnerships and Their Impacts on Communities of Color." Upturn.org, 2020.

Upturn, a Washington, D.C.-based nonprofit focused on civil liberties and technology, produced this policy report analyzing Ring's law enforcement partnerships specifically in terms of their disparate racial impact. The report includes case studies of racially biased Neighbors posts and analysis of Ring's contract terms. Available free at upturn.org.

9. Tokson, Matthew. "The Emerging Principles of Fourth Amendment Privacy." Georgetown Law Journal, 2020.

A doctrinal analysis of how the Supreme Court's Carpenter decision affects the third-party doctrine and what implications the decision has for data collected by home devices like Ring cameras. Tokson argues that Carpenter signals a move toward a more nuanced privacy analysis that considers the sensitivity and intimacy of data collected, not just whether it has been "shared" with a third party. Law review databases.

Book-Length Context

10. Zuboff, Shoshana. The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power. PublicAffairs, 2019.

Zuboff's influential analysis of surveillance capitalism — the economic logic by which behavioral data becomes the primary commercial commodity — provides the broadest theoretical context for Ring's data practices. While Zuboff's work does not focus on Ring specifically, her analysis of how intimate behavioral data is extracted through consumer products, rendered as predictive analytics, and sold as "behavioral futures" illuminates the economic logic that makes Ring cameras strategically valuable to Amazon beyond their direct revenue. Undergraduate readers may find Chapters 1–5 and 11–13 most directly relevant; the full text rewards sustained engagement.

For Students Interested in Going Deeper

Surveillance Studies Network (www.surveillance-studies.net): An academic organization whose journal, Surveillance & Society, publishes peer-reviewed research on surveillance across all dimensions examined in this textbook. Freely available online.

Electronic Privacy Information Center (EPIC, www.epic.org): Maintains detailed case files on Ring, Amazon, and related surveillance topics, including ongoing litigation, regulatory proceedings, and congressional testimony.

The Markup (www.themarkup.org): A data journalism organization that has published systematic analyses of Ring camera distribution, racial bias in neighborhood surveillance apps, and algorithmic decision-making in policing.