We Are Data: Algorithms and the Making of Our Digital Selves – Review

There’s a lot of talk about data and algorithms in mainstream discourse these days. Even if you don’t follow data scientists and digital media scholars on Twitter (like I do), you’ll be hard-pressed to find someone with a social media account that hasn’t at least heard about the Cambridge Analytica/Facebook scandal.

*Here’s a good primer from the New York Times, if you want a refresher. 

Understanding, on the other hand, can be elusive for those who don’t study the implications of digital technology on society and culture. This is a topic that interests me greatly. To be clear, I don’t claim to be a scholar on this topic… but I do think it’s important enough that everyone should at least try to understand it, esoteric as it is. Which is why I was so pleased with John Cheney-Lippold’s 2017 book, We Are Data: Algorithms and the Making of Our Digital Selves.


John Cheney-Lippold is Assistant Professor of American Culture and Digital Studies at the University of Michigan, but don’t let his position in academia dissuade you from reading this book. It’s not overly theoretical. In fact, one of the most impressive things about it is how accessible it is to a layperson, like myself (and like most people I know).  These are complicated topics, and the implications being discussed can often be abstract, but Cheney-Lippold makes them relevant by using anecdotal narratives to illustrate how you and people you know could be affected in the real world.

Particularly poignant examples include the baked-in racism of predictive policing algorithms and real-world drone strikes against individuals labeled ‘terrorist’ by a proprietary State Department algorithm.

So what’s the big deal with algorithms collecting our personal info, anyway? Isn’t it just about targeting ads based on our browser preferences? That is one aspect of it (and the most benign, in my opinion). But like most things, it goes so much deeper than that…

At the heart of Cheney-Lippold’s argument is the idea of a “black box society” in which only the developers of the algorithms have the right and the ability to know how an algorithm works—what data it collects, how it collects it, and how it creates models based on that data. The book explores the consequences of proprietary algorithms creating our digital identities, how those identities are subject to control, and the available methods to resist that control. Throughout, Cheney-Lippold draws on the theories of Michel Foucault, Tiziana Terranova, Judith Butler, and many others to illustrate how the algorithmic interpretation of an individual’s data, the details of which are shielded from public scrutiny, can effectively control individuals’ behavior both online and offline.

Heavy stuff, right? And I won’t lie… reading this book is still a bit of a chore, despite how accessible it is. There are two things Cheney-Lippold did that makes me want to get everyone I know to read this book:

  1. He repeats himself. He constantly references the same examples, studies, and concepts throughout the book to ensure the reader remembers and connects what he is talking about. This rhetorical technique is significant because it’s unlikely anyone will get through this book in one or two sittings. It took me about three weeks (admittedly I was in grad school when I read this, but even had I not been, it’s too much information to digest in a short amount of time).
  2. He divides the discussion into digestible chunks. The book contains four in-depth discussions that Cheney-Lippold skillfully relate to one another:
    1. Categorization: “how governments, corporations, and researchers use algorithms to make data useful” (p. 46).
    2. Control: “The control in this chapter is not a control that guides you against some presumed, autonomous will. Instead, it’s a control that frames your world” (p. 148).
    3. Subjectivity: “The job of this chapter is to outline the problems, possibilities, and theoretical issues that arise when we are made subject (or made ‘citizen’) according to relations of data and only data” (p. 158).
    4. Privacy: “what does privacy look like now that we are more than just our bodies but also our information?” (p. 236-237).

Part II, Control, and Part IV, Privacy, are probably the most relevant to mainstream discussions going on right now. Part III, Subjectivity, is admittedly the most abstract (but important prior to getting into Privacy).

What I came away with was a deeper understanding of what I, as a digital citizen, am subjecting myself to with each decision I make to interact online, along with a better idea of how to resist methods of control, should I wish to. We Are Data introduces a lot of questions about where we are in digital culture and what we may be able to do about it. It doesn’t necessarily provide answers to these questions, but it does provide a good framework to help think about these problems, which we all should be.

We Are Data: Algorithms and the Making of Our Digital Selves comes out in paperback in November of 2018 and can be found in the Digital Studies section of the 2nd floor at BookPeople.

Other resources you might be interested in:

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2 thoughts on “We Are Data: Algorithms and the Making of Our Digital Selves – Review

  1. Thanks Sarah. When you write, “the algorithmic interpretation of an individual’s data can effectively control individuals’ behavior both online and offline.”, I guess we are all inclined to think that it can control us in a “bad way” (maybe not ?), or to say it another way, alienate ourselves : making us buy things we never though of, giving statements on our bad behaviours, rating our social environments, etc. But do you remember reading anything about algorithms empowering citizens ?

    1. it’s been a minute since I read this, so I’ll do my best to answer your question.

      the focus of this book is certainly on the current state of how algorithms are used online, which is largely in an opaque and proprietary way. The author’s argument, imo, indicates that this specifically disempowers people. I believe he does make a distinction between algorithms that can be (and often are) used in beneficial ways and ones that aren’t (he discussed the case of the Google Flu tracker, which was an example of good intentions on a project that not only failed to deliver but had a number of unforeseen implications that could have been socially dangerous).

      But, the focus is on the fact that the public has no access to know how proprietary algorithms build their models, and so there is no way to discover how companies are using our data or to analyze the legitimacy of the algorithm’s results.

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