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Book Review: Afterlives of Data

Book review of Mary F. E. Ebeling's Afterlives of Data: Life and Debt under Capitalist Surveillance (2022)

Published onDec 06, 2022
Book Review: Afterlives of Data

Afterlives of Data: Life and Debt under Capitalist Surveillance, by Mary F. E. Ebeling (2022). Oakland, CA: University of California Press.   

Afterlives of Data is a story of alienation in a data-based society, exploring the “intertwined regime of debt and medicine” (p. 133), where personal medical data dispossessed from our body is combined with debt data to generate profits for private corporations. Citing ethnographic findings collected at a university-based medical research institute in the United States, sociologist Mary F. E. Ebeling describes this profit-extracting process whereby medical data comes from our bodies and becomes private firms’ tradable assets, which are then used to calculate credit and health-risk scores and, in doing so, come to shape our lives. She further argues that government policies, legislation, corporate lobbies, and ideologies are all enmeshed in order to generate, sustain, and operate our data-based society.   

Throughout the chapters, Ebeling traces the long journey of health data to the point where it is combined with debt data. Health data starts its first afterlife once it has been separated by healthcare providers from patients’ bodies. Under the Health Insurance Portability and Accountability Act (HIPAA), medical data that includes patients’ identifiable information, called Protected Health Information (PHI), can be shared with hospitals, insurers, pharmacies, and other institutions within the healthcare industry, which are defined as “covered entities” by HIPAA. Even though PHI should be de-identified when it is shared outside of the healthcare industry, this information often is circulated via commercial contracts between covered entities and private firms, such as Google and Experian. Drawing from several examples of commercial contracts between Google and medical institutions, Ebeling shows that patients’ data is not being protected with non-disclosure agreements or confidentiality contracts. After this data leaves the healthcare industry, it is instead combined with other datasets gleaned from social media, web scraping, and credit scores. This is the point where debt and health are intertwined; the debt and the healthcare industries need each other’s data for building more accurate algorithmic decision-making tools that span social and health-risk information. In other words, just as the debt industry uses the health data to feed algorithms scoring individuals’ credit risk, health facilities use credit data to predict patients’ adherence to medical directives as well as their ability to pay for medical treatment. Based on these “facts” or scores made by algorithms, the debt and healthcare industries shape individuals’ range of choices, such as their access to loans or particular medical treatments.

However, data is not the only character in this book. When datasets become free from our bodies, flow into data oceans, and gain their own momentum, lawmakers and corporate executives take on an increasingly important role in this process. As Ebeling discusses, from the birth of HIPAA, patients’ privacy has not been the first concern; rather, she says an encroachment on data privacy is “not a bug but a function of the legislation” (p. 53). Under HIPAA, patients do not give their informed consent to sharing data. Furthermore, although patients’ identifiable information should be de-identified when it is shared outside of covered entities, such as to tech firms like Google, entities considered as “business associates” of covered entities can use patients’ identifiable information under the Omnibus Act of 2013. Similarly, the Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009 incentivized healthcare providers to digitalize their data-management systems, leading the creation of information oceans in which patients’ data can be mined and assetized. Importantly, she pinpoints that Big Tech firms – such as Amazon, Facebook, Apple, and Google – have dedicated astronomical amounts of money to lobby for, and draft, favorable legislation surrounding these activities. In this way, Ebeling’s book shows that political and corporate power are deeply involved in opening the health-data market and facilitating the trade of data at the expense of patients’ privacy. 

Ebeling also points out that the commercialization of patients’ data is accompanied with ideological justification. She illustrates that the Open Government Initiative of the Obama administration, designed to transform all government information into machine-readable data, was developed and implemented in the name of citizen empowerment, democratization, and better health outcomes. However, Ebeling also pointed out that those who enjoy the benefits of this rosy promise are not patients but covered entities, tech firms, and credit bureaus. Blind faith in the supposed unbiasedness of algorithms is another ideological glue described in Ebeling’s book, binding together raw power and alienated data. Credit and health-risk scores, which are based on racially and socio-economically biased data, are often believed to be “objective” and outperform human decisions. As “these falsehoods harden into facts” (p. 28) and decisions are automated by them, patients with low credit scores become more exposed to risks by receiving inappropriate treatments or being denied health services, potentially leading to the exacerbation of economic and health inequality. 

In the data-based society in which we live, our multi-faceted social lives are captured and codified on a real-time basis. Focusing on the many crossroads in this digital grid, Ebeling’s book sheds light on the point where our health and debt data meet and shows how this combined amalgam is alienated from and restricts us. Given that many aspects of our lives (such as politics, work, and entertainment) are increasingly digitalized, this book’s valuable insights into how data, power, and ideology fortify each other in today’s data-based society can be extended to many other fields within the social sciences.


Author Biography:

Jeonghun Kim is a Ph.D. student at the Cornell ILR school. He is interested in how labor movements respond to the changing nature of work. His main research interests are about how workers come to be controlled by digital platforms but also how they resist against them. Currently, he is working on a study on comparing the different narrative strategies of two food-delivery unions in South Korea.



Jack Ali:

The second step in saw blade maintenance is to ensure that the blade is kept clean. This can help prevent sawdust and debris from clogging the teeth of the blade and causing it to become jammed. To clean a saw blade, use a damp cloth or brush to remove any built-up sawdust. If the blade is particularly dirty, you can use a brush and a solution of mild detergent and warm water to scrub away any debris. Once the blade is clean, apply a thin layer of oil or wax to the blade to help protect it from rust.

Harold Wilson:

The debt and healthcare businesses need to hire us to develop more precise algorithmic decision-making tools.

Billie JOllie:

Afterlives of Data explores the interesting and varied lives that our data lead once word wipe they are released from our control. The ethnographic research of Mary F. E. Ebeling demonstrates how healthcare providers, insurers, commercial data brokers, credit reporting agencies, and platforms come to control our health and debt information as biopolitical assets.