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WORK TITLE: We Are Data
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https://lsa.umich.edu/ac/people/faculty/jchl.html * https://www.fromthesquare.org/we-are-data/#.WgpWQsanFPY * http://blogs.lse.ac.uk/impactofsocialsciences/2017/10/01/book-review-we-are-data-algorithms-and-the-making-of-our-digital-selves-by-john-cheney-lippold/
RESEARCHER NOTES:
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University of Southern California, Ph.D., 2011.
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University of Michigan, Ann Arbor, MI, assistant professor of American culture and digital studies.
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John Cheney-Lippold is an assistant professor at the University of Michigan. His research interests include Internet studies, cultural studies, and code and algorithm studies.
“Online you are not who you think you are,” states Cheney-Lippold in his first book, We Are Data: Algorithms and the Making of Our Digital Selves. Everything individuals do online, the author explains, is collected and stored; this information is then made available to search engines, government agencies, and various marketers, who use it to assign what the author terms “categorical meaning” to individual lives. These meanings can include gender, educational level, financial status, and more private details such as health condition and personal trustworthiness. Each click on a website, says the author, contributes to a cumulative digital portrait—“gay conservative,” for example, or “white hippy”—that can be used to market products or to control behavior. But these online identities are necessarily unreliable. As the author states in the book’s introduction: “We are ourselves, plus layers upon additional layers of … algorithmic identities. Algorithmic interpretations like Google’s ‘celebrity’ identify us in the exclusive vernacular of whoever is doing the identifying. … And like being an algorithmic celebrity and/or unreliable, when our embodied individualities get ignored, we increasingly lose control not
just over life but over how life itself is defined.”
These algorithmic identities can also be completely wrong. Google’s algorithms, for example, might interpret Cheney-Lippold’s online activity to conclude that he is male, while a marketing site’s algorithms could conclude that he is female.
Cheney-Lippold draws on recent scholarship in the fields of philosophy, digital theory, history and legal studies, anthropology, queer theory, and political science to explore the significance of data collection and its meanings. While targeted marketing may seem relatively harmless or even welcome to some, algorithms are also used in more troubling ways. These include such things as allocation of health care; predictive policing; hiring practices; and mortgage approvals. The U.S. government categorizes people as either citizens of foreigners based on their e-mail and Twitter activity. Simply having several overseas addresses in one’s list of e-mail contacts, for example, increases the level of that person’s foreignness according to the government’s metric.
The author also discusses how algorithms are used to change information. Media sites, for example, collect demographic data about users and shape their content accordingly. A site such as the London Guardian’s, for example, might choose to continue publishing stories targeted to its core audience of white males; alternatively, it might prefer to attract a more diverse audience and therefore choose to publish different content to achieve this goal.
We Are Data concludes with a chapter on privacy. One way for individuals to take control of their online identities, Cheney-Lippold writes, is to flood the algorithmic systems with random and irrelevant data, making it impossible for these systems to make accurate predictions about a person’s categories. TrackMeNot and Tor are two applications designed to confuse algorithmic systems in this way. “The purpose of writing my book,” Cheney-Lippold explained in an interview on the website From the Square, “was to demonstrate how profiling, surveillance, and machine learning are formatively changing not just what the Internet looks like, but literally who we are. … I aim to push readers to think about how digital surveillance actively reconfigures who you are, and who you can be, according to the latest trends found in your latest data.”
Commentators hailed We Are Data as an enlightening and important work. In Kirkus Reviews, a contributor described the book as a “heady and rewarding exploration of our lives in the data age.” Daniel Zwi, writing in the London School of Economics and Political Science’s blog, concluded: “If knowledge is indeed the means by which we can begin to challenge the digital status, quo, then Cheney-Lippold has done much to forearm us by so capably elucidating the problem.”
BIOCRIT
BOOKS
Cheny-Lippold, John, We Are Data: Algorithms and the Making of Our Digital Selves, New York University Press (New York, NY), 2017.
PERIODICALS
Kirkus Reviews, April 1, 2017, review of We Are Data: Algorithms and the Making of Our Digital Selves.
ONLINE
From the Square, https://www.fromthesquare.org/ (May 3, 2017), interview with Cheney-Lippold.
London School of Economics and Political Science Website, http://blogs.lse.ac.uk/ (October 1, 2017), Daniel Zwi, review of We Are Data.
University of Michigan, College of Literature, Science, and the Arts Website, https://lsa.umich.edu/ (February 2, 2018), faculty profile.
An Interview with John Cheney-Lippold, Author of We Are Data
May 3, 2017 nyupressblog American Studies, Media Studies 0
A Q&A with John Cheney-Lippold, author of We Are Data: Algorithms and The Making of Our Digital Selves.
What is posthumanism and how does it shift emphasis from the contained material body to information?
Cheney-Lippold: Posthumanism is a theory that what makes us up extends beyond our human bodies to include information about us. This is seen in how one’s credit score, which is algorithmically produced, determines if or if not you can buy a house. Ostensibly, this decision has no connection to your body, but demonstrates how one’s self reaches outside one’s body and into the confines of databases and archives. As digital surveillance accumulates more and more data about ourselves, the power of this shift toward information becomes ever more important.
You suggest that our data doesn’t reveal a Truth about ourselves, but instead our “truth” is told to us by an algorithm’s output. How do the terms of information become the terms of our emergent digital subjectivity? How is this reconceptualization of ourselves used to categorize and control us?
CL: When we know who we are—and when we know how who we are regulates how we are seen—we know a great deal about how the terms of our life: what we can do, cannot do, and need to do. When who we are is determined by algorithm, these terms are unknown to us. In this way, whatever an algorithm says we are, we are. There is no truth, but rather a constantly moving “truth” that changes according to our data and how it gets algorithmically valued.
Data transforms an individual into what you call a “dividual.” What does that mean?
CL: We are first dividuals online, not individuals. This is because users of the Internet have no suitable index that connects them to their individuality (two people can easily use the same computer, email account, or Facebook account). Accordingly, identity online comes from connecting different dividual fragments of our data together in order to create an idea of the user. Three dividual fragments of us, then, might be our IP address’ location, the search term we just searched for, and the time of day we searched for it. These fragments, which mean little by themselves, could go on to create an idea of who you are that then determines you to be a “citizen” of the US or a “foreigner”. In this case, while our individualities have histories (and passports) that we use to directly intercede in defining who we are, our dividualities, when algorithmically assembled, produce an idea of our selves likely alien, and unintelligible, to us.
How does who we are, algorithmically speaking, change minute by minute, byte by byte according to the interpreter’s proprietary formula and agenda?
CL: Much like in a conversation, where every additional word ideally helps you understand more about the person you are speaking with, every new piece of data you produce adds to the potential meaning of who you are. But, of course, data can be wrong much like people can lie, cheat, or steal. It’s up to algorithmic pattern-matching to determine what is a useful piece of data (one that might change your algorithmic “gender” from “man” to “woman”), or what is a useless meaningless piece of data (one that might have no effect on your algorithmic “gender” at all). But much like one’s identity can change—based on a new website visit or product purchase—the categories of “man” and “women” themselves change, as users characterized as “man” or “woman” do new things, en masse, to collectively redefine the category of “gender”.
Are we unconsciously facilitating our constant contact with an invisible regulatory regime that effectively recalibrates the nuances of who we are in order to define and control us?
CL: I think the only way Internet users are willing to accept such a wide-reaching regulatory regime is because we do not see, and thus unconsciously facilitate contact with it. The purpose of writing my book was to demonstrate how profiling, surveillance, and machine learning are formatively changing not just what the Internet looks like, but literally who we are. Many people learn about digital surveillance and attribute it to “just” targeted advertising. I aim to push readers to think about how digital surveillance actively reconfigures who you are, and who you can be, according to the latent trends found in your latest data.
Algorithms depend on the dividual bits of data we produce rather than knowledge of an integrated individual. Should we be less concerned then about our need for online privacy? How do privacy needs for our body differ from privacy needs for our information?
CL: One privacy is not necessarily more important, or deserving of more concern than another. While bodily privacy is incredibly important, I want to focus, instead, on how we can begin to understand what dividual privacy is and can be.
To focus on informational or dividual privacy is to foreground how privacy is more than just preventing somebody from looking at you in the bathroom. Privacy in the U.S. historical context has been what we call the right “to be let alone.” As such, while we might be alone in our room on our computers, the information about who we are is rarely, if ever, “let alone”. Data about ourselves might make us a “citizen” who has the right to privacy or a “foreigner” who doesn’t; data about our screen size or operating system might make us pay extra on a plane ticket because we are seen to be “wealthy”. Importantly, these pools of data necessarily change according to new web site visits, search queries, or GPS locations. We are forced to live in a digital world that we can never really know—and we thus lose the ability to be “let alone”.
Policing and security agencies can create “predictive policing” algorithmic categories to label certain people as “at risk” for committing crime. How are these algorithms dependent on racial and economic stereotyping and how does it run afoul of profiling laws?
CL: Police agencies that use predictive policing, like the Chicago Police Department, claim individuals are “at risk” because their data is connected to other “at risk” individuals. The central problem that spoils this idea is that the CPD’s arrest data does not cover the entire Chicago population equally.
Due to a host of reasons, poorer people are more likely than wealthier people to be detained and arrested by police. And, due to a host of reasons, people of color are more likely than white people to be detained and arrested by police. And lastly, due to a host of reasons, the neighborhoods where poorer people of color live are patrolled more vigilantly than wealthier white areas. If the data that the CPD uses to create “at risk” comes from a biased data set, then the algorithmic outputs from that data will necessarily be burdened by economic and racial prejudice.
How does algorithmic identity reinforce stereotypes or, worse, profiling and racism?
CL: A funny, but extraordinarily problematic, example of stereotypes/racism is found in the book’s introduction through the video “HP Computers are Racist”. In this video, two individuals are in front of an HP computer with facial recognition technology. The white individual, Wanda, is identified by the camera; the black individual, Desi, is not. As it turns out, the data of a black man’s face didn’t fit the algorithmic identity of a “face”, and thus the algorithmic pattern defining a “human”. This explicit example of racism was, by many, technologically white-washed away: defenders of HP claimed the error in recognizing Desi wasn’t about race, but about resolvable lighting problems.
But this answer is to ignore some very real, and important, structural facts about the world, particularly: algorithms and data are not neutral. Both come from lived histories and conflicts, including racist ones, that unintentionally (or intentionally) seep their way into the digital realm. The fact that Desi wasn’t recognized could be due to the low numbers of black engineers in Silicon Valley companies, and/or it could be due to HP not thinking about racial differences while developing their facial recognition product, and/or it could be due to HP using betatesters who were lighter skinned. Nonetheless, the power relationships that order and define us in the offline world are always, and will always be, connected to the power relationship that define us online.
What is algorithmic citizenship? How does data determine our temporal, informationalized citizenship and how does it affect our privacy rights against government surveillance?
CL: Algorithmic citizenship is a mode of identification that governments use to determine users’ citizenship status when no documentation is available. This audacious algorithmic identification attempts to recreate the idea of the “ideal citizen”, and thus the “ideal foreigner”, through an algorithmic analysis of available data: where one’s IP address is, to who one talks to and even to what language one speaks. In terms of privacy, NSA documents have shown that a user can be legally surveilled if their traffic is deemed to be “51% confidence foreign”. Because one’s “citizenship” is based on data—and only data (and not some permanent identity card of birth certificate)—it changes with every person you talk to, every time you travel abroad/cross borders, and even every time you change which language you speak.
How can we protect our privacy in a world where surveillance is everywhere yet rarely felt? How can we break free of being algorithmically dominated and manipulated?
CL: The strategy I propose in the book is twofold. First, surveillance affects different groups of people differently—privacy strategies against surveillance need to take into account how race, gender, class make surveillance tougher on some than on others. Second, because of digital surveillance’s ubiquity and the technological impossibility of being truly unmonitored online, I suggest we aim our privacy practices toward confusion, not avoidance: producing aberrant or random data in order to confuse those algorithms that pattern assess our data. This strategy, which some scholars have called obfuscation, works by generating random, meaningless data automatically in an effort to disorient the possibility for any cohesion of identity that could be then used to determine who you are—and how to manipulate you.
By tracking our data, we are remembered forever. In Europe, citizens have been granted the right to be forgotten and can petition companies like Google to remove their information from databases. Do right to be forgotten laws apply here in the US? What are some of the differences between European and US ideas of privacy?
CL: The “Right to be Forgotten” stakes out an interesting future for privacy because it harkens back to privacy’s origins, while trying to think through those origins in the digital sphere. These laws do not apply to the US, whose lawmakers are now aggressively rolling back individual privacy protections.
In terms of comparison: despite the legacies of US privacy law and the 4th Amendment, domestic norms of privacy are very much aligned with an American rhetoric of individualism combined with a post 9/11 racializing sensibility: if one deserves privacy, it’s because they’ve earned it by being a good citizen; if one doesn’t deserve privacy, it’s because they don’t need it, anyway—they shouldn’t have anything to hide. European ideas are much more collective and public-oriented, which comes from a certain expectation that all individuals in society should have privacy. In the case of the European Union’s Constitution, privacy is a formal right, while the US’s “right” to privacy is theorized and inferred, but never stated.
Hundreds of companies and agencies identify a person algorithmically in different ways, meaning that there may be thousands of versions of one person, each defined by a different gender, race, class, etc. depending on the algorithm used. What is the danger of being redefined from an individual subject to a category-based profile that is remapped every day as mathematically equilibrated aggregate of knowledge? Is there such a thing as authenticity online?
CL: First, there is no such thing as authenticity online, as the metrics we use to figure out who we are (what you say you are, what you want to do) are muddled by the fact that what one says one is doesn’t matter, and what one wants to do online is directed by how one is seen.
Second, the danger of this kind of redefinition of subjectivity is that one never truly knows who they are. If “gender” for Google is different than “gender” for Microsoft, and both algorithmically change every day in order to follow the new trends of what their data says “gender” is, then the ability to know oneself—to understand who one is and how one is being treated—is impossible. This impossibility is what the book is about; we shouldn’t try to avoid it, as it is a Sisyphean task, but rather we should learn how to respond to it, and productively resist it.
What information are companies and advertisers determining about us when we “Like” something on Facebook?
CL: To “like” something on Facebook is to generate data that connects a user to a certain concept, product, person, or place. Facebook uses “likes” to then place users in different boxes (a user is “Liberal” because they “like” the environment; a user is also “Hispanic” because they “like” tango music) that then allows advertisers on Facebook to target their ads to Liberal Hispanics. But more than merely producing datafied value, “likes” are used to create these boxes themselves. No one told Facebook that Hispanics “like” tango. Rather, known-Hispanic users “liked” tango at a disproportionate rate, suggesting that to “like” tango means to be “Hispanic”.
How we live and experience something as multifaceted and multilayered as the connections between gender, race and class is reduced to statistical quantitative terms in algorithms. How does this quantification deprive us of the opportunity to critique and challenge these categories?
We Are DataCL: Both marketers and political theorists have realized that addressing a person only through the lens of gender, OR the lens of race, OR the lens of class, doesn’t get, at all, to the lived experience we have when that person is addressed through the intersection of gender AND race AND class. This intersectional theory, as it is called, allows us to critically understand how, for example, women are not all treated the same by institutions of power like the police, health system, or legislature. For example, domestic abuse laws for all women in general fail to understand that women who are wealthier likely will have a different experience with the legal system than women who are poorer.
When these categories are made through data and statistics (where a user is “Hispanic” just because they “like” tango music)—and instead of lived experience—this intersectional approach loses its political teeth. When we don’t know what, exactly, makes up a “woman” in terms of data, or what, exactly, makes up “wealthy” in terms of data, the critical potential to see how race matters, class matters, or gender matters, becomes impossible. The identity of those categories are owned by companies like Google—not us. In summary, they lose their politics.
We Are Data is available now!
John Cheney-Lippold is Assistant Professor of American Culture and Digital Studies at the University of Michigan.
John Cheney-Lippold
Assistant Professor
jchl@umich.edu
Office Information:
3527G Haven Hall
Fields of study:
Internet studies, cultural studies, code and algorithm studies
phone: 734.764.6799
Education/Degree:
Ph.D., University of Southern California, 2011
John Cheney-Lippold has picture
About
Professor Cheney-Lippold is teaches and writes on the relationship between digital media, identity, and the concept of privacy. He is the author of We Are Data: Algorithms and the Making of our Digital Selves (NYU Press, 2017).
Research Areas(s)
gender
race
citizenship
identity
surveilance
code
algorithm
privacy
Affiliation(s)
Faculty: Department of American Culture (AC) Digital Studies (DS)
Digital Environments Cluster (DEC)
Field(s) of Study
Internet studies, cultural studies, code and algorithm studies
Cheney-Lippold, John: WE ARE DATA
Kirkus Reviews. (Apr. 1, 2017):
Copyright: COPYRIGHT 2017 Kirkus Media LLC
http://www.kirkusreviews.com/
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Cheney-Lippold, John WE ARE DATA New York Univ. (Adult Nonfiction) $27.95 5, 2 ISBN: 978-1-4798-5759-3
How algorithms shape our lives online.There's you--the real-life you--and there's "you" online, as defined by algorithms that track every digital step you take and, depending on the data collected, assign your gender, age, educational level, and more. Few aspects of this "scary and intriguing" situation, as Cheney-Lippold (American Culture and Digital Studies/Univ. of Michigan) quite properly calls it, are overlooked in his debut, a heady and rewarding exploration of our lives in the data age. "Online you are not who you think you are," he writes. Instead, based on information "observed, recorded, analyzed, and stored" in a database, your life is assigned "categorical meaning," whether by Google, a government agency, or any number of marketers: you are deemed unreliable, or a celebrity, or whatever, without your knowledge or any regard for who you really are. Thus you are "datafied" into computable data, which is used (by those with the power to do so) to "market, surveil, or control us." Furthermore, your datafied identity is ever changing, depending on your latest online clicks. "Data holds no significance by itself--it has to be made useful," writes the author. "We are thus made subject not to our data but to interpretations of that data." Drawing on the work of a mind-boggling array of specialists, including philosophers, digital theorists, historians, legal scholars, anthropologists, queer theorists, and political scientists, Cheney-Lippold explores how companies and governments use our datafied identities in marketing, predictive policing, and in such matters as race and citizenship. His discussions of privacy in such a world--and of the fact that we are "not individuals online; we are dividuals"--will fascinate and unnerve many. In complex, thoroughly researched chapters, the author explains how this ceaseless interpretation of data by organizations that find it useful for their own purposes is setting the parameters for our present and future lives. Essential reading for anyone who cares about the internet's extraordinary impact on each of us and on our society.
Book Review: We Are Data: Algorithms and the Making of Our Digital Selves by John Cheney-Lippold
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In We Are Data: Algorithms and the Making of Our Digital Selves, John Cheney-Lippold examines how algorithms increasingly interpret and influence our behaviour. With the author concluding with some pragmatic suggestions for challenging the digital status quo, Daniel Zwi welcomes the book for both capably elucidating the problem of algorithmic regulation and forearming us to tackle this issue.
This review originally appeared on LSE Review of Books and is published under a CC BY-NC-ND 2.0 UK license.
We Are Data: Algorithms and the Making of Our Digital Selves. John Cheney-Lippold. NYU Press. 2017.
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In 2013, a 41-year-old man named Mark Hemmings dialled 999 from his home in Stoke-on-Trent. He pleaded with the operator for an ambulance, telling them that “my stomach is in agony”, that “I’ve got lumps in my stomach”, that he was vomiting and sweating and felt light-headed. The operator asked a series of questions — “have you any diarrhoea or vomiting?”; “have you passed a bowel motion that looks black or tarry or red or maroon?” — before informing him that he did not require an ambulance. Two days later Mr Hemmings was found unconscious on the floor of his flat. He died of gallstones shortly after reaching hospital.
This episode serves as the affective fulcrum of We Are Data: Algorithms and the Making of Our Digital Selves, John Cheney-Lippold’s inquiry into the manner in which algorithms interpret and influence our behaviour. It represents the moment at which the gravity of algorithmic regulation is brought home to the reader. And while it may seem odd to anchor a book about online power dynamics in a home telephone call (that most quaint of communication technologies), the exchange betokens the algorithmic relation par excellence. Mr Hemmings’s answers were used as data inputs, fed into a sausage machine of opaque logical steps (namely, the triaging rules that the operator was bound to apply), on the basis of which he was categorised as undeserving of immediate assistance.
The dispassionate, automated classification of individuals into categories is ubiquitous online. We either divulge our information voluntarily — when we fill out our age and gender on Facebook, for example — or it is hoovered up surreptitiously via cookies (small text files which sit on our computer and transmit information about our browsing activity to advertising networks). Our media preferences, purchases and interlocutors are noted down and used as inputs according to which we are ‘profiled’ — sorted into what Cheney-Lippold calls “measureable types” such as “gay conservative” or “white hippy” — and served with targeted advertisements accordingly.
The effects of this ecosystem extend beyond the vague sense of unease we feel when, for example, a Facebook advertisement for flights to Germany appears on our laptop after we have searched for Berlin accommodation on our Airbnb mobile app. “We kill people based on metadata”, says a former NSA chief in We Are Data, explaining that all it takes to be categorised as a “terrorist” is for your communications footprint to match a relatively arbitrary and algorithmically-determined assemblage of data points. You live in Somalia, rent a truck, buy ammonium nitrate fertiliser and talk to someone who’s talked to someone unsavoury, and before you know it you’re fair game for drones.
Image credit: Nullen und Einsen aus Glas auf Metal mit violettem Filter- IT – Computer -Data by Christoph Scholz. This work is licensed under a CC BY-SA 2.0 license.
Cheney-Lippold describes a world in which algorithms giveth and taketh away. The US government categorises internet users as “citizens” or “foreigners” based on the extent to which their online activity conforms to the NSA’s template for each group. Communicating with overseas accounts on Twitter, having an email contacts list which includes people reasonably believed to live outside the US, even sending messages in a language other than English: all these traits increase one’s “foreignness” according to the NSA’s algorithmic calculus. Needless to say, only those deemed citizens are afforded a full complement of constitutional rights online.
We Are Data makes plain, then, that profiling is not just about shifting products. But the preponderance of the book’s content attends to the more nuanced ways in which algorithms regulate our lived experience. In a process termed “modulation”, Cheney-Lippold argues that profiling exerts control via the allocation of knowledge itself. By this he means more than simply the phenomenon of online echo chambers, whereby internet users are increasingly exposed to media outlets and acquaintances who share and thus entrench their views. Profiling, he emphasises, governs the production of information as well as dictating who gets to consume it.
Thus, when a “white man” visits the Guardian website, its aggregate demographic profile becomes commensurately whiter and more male. Faced with this information, an editor can either double down on their existing content to retain this core audience group or they can publish new kinds of material in order to attract, say, “black women”. In either case, the algorithm has done more than merely steer content towards those inclined to accept it. Profiling influences what Michel Foucault calls society’s “discursive topography”, modulating public and media discourse and thus delimiting what can be uttered and understood.
If this all sounds a bit dire, I suppose that’s because it is. But the final chapter, on privacy, contains Cheney-Lippold’s prescription for wresting back control online. He endorses a technique that Helen Nissenbaum has called “obfuscation”, involving the flooding of an algorithmic system with extraneous and arbitrary data in order to compromise its predictive capacity. In concrete terms, this could mean utilising applications such as TrackMeNot, which, every six seconds and on four different search engines, launches random queries from your browser. Tor is also identified as an effective means of stymieing the formation of profiles.
It’s a shrewd way to finish the book. As well as presenting grounds for optimism, the chapter rounds off a neat transition from the abstract (previous sections focus on categorisation, control and subjectivity) to the pragmatic. Having said that, there are stylistic tics in We Are Data that distract from the logic of the structure. New authors to which the text refers are invariably given their own pedagogical epithet. We are introduced to the work of “digital theorist Seb Franklin”, “media scholar Joseph Turrow” and “literary critic N. Katherine Hayles”. Aside from feeling a bit gratuitous, Cheney-Lippold quickly runs out of labels, such that towards the end of the book authors are somewhat meaninglessly described as “theorist Tiziana Terranova” or “scholar Victor Mendoza”.
There are also relevant legal debates whose absence from the chapter on privacy is conspicuous. To be sure, We Are Data is not a legal text, and the author cannot be expected to cover every discipline’s approach to algorithmic regulation. But the chapter begins with a detailed account of how US law is inadequate for ensuring that users know when, how and why they are profiled. A short discussion of EU measures which have been introduced precisely to ensure such disclosure would have enriched the argument.
In the book’s conclusion, Cheney-Lippold characterises Mark Hemmings’s death as a tragedy of knowledge. The telephone conversation is so disturbing because he had no idea that his answers were being used as algorithmic inputs: that they were being datafied, quantified and processed unilaterally to determine his fate. There is something nauseating about informational asymmetry on this scale, particularly when so much is at stake. But if knowledge is indeed the means by which we can begin to challenge the digital status quo, then Cheney-Lippold has done much to forearm us by so capably elucidating the problem.
Daniel Zwi is a lawyer with an interest in human rights and technology. You can find him on Twitter @dan_zwi.
Note: This review gives the views of the author, and not the position of the LSE Impact Blog, or of the London School of Economics.
From book's introduction:
. Who we are, following
Internet researcher Greg Elmer’s work on “profiling machines,”
is also a declaration by our data as interpreted by algorithms.7 We are
ourselves, plus layers upon additional layers of what I have previously
referred to as algorithmic identities.8
Algorithmic interpretations like Google’s “celebrity” identify us in the
exclusive vernacular of whoever is doing the identifying. For the purposes
of my analysis, these algorithmic categorizations adhere to what
philosopher Antoinette Rouvroy calls “algorithmic governmentality”—a
logic that “simply ignores the embodied individuals it affects and has as
its sole ‘subject’ a ‘statistical body’. . . . In such a governmental context,
the subjective singularities of individuals, their personal psychological
motivations or intentions do not matter.”9 Who we are in the face of
algorithmic interpretation is who we are computationally calculated to
be. And like being an algorithmic celebrity and/or unreliable, when our
embodied individualities get ignored, we increasingly lose control not
just over life but over how life itself is defined.
This loss is compounded by the fact that our online selves, to borrow
the overused phraseology of pop psychology, is a schizophrenic
Cheney_Lippold_i_317.indd 5 2/22/17 12:39 PM
6 < I ntroduction
phenomenon. We are likely made a thousand times over in the course
of just one day. Who we are is composed of an almost innumerable collection
of interpretive layers, of hundreds of different companies and
agencies identifying us in thousands of competing ways. At this very
moment, Google may algorithmically think I’m male, whereas digital
advertising company Quantcast could say I’m female, and web-analytic
firm Alexa might be unsure. Who is right? Well, nobody really.
Stable, singular truth of identity, also known as authenticity, is truly
a relic of the past. Our contemporary conception of authenticity, as argued
by feminist scholar Sarah Banet-Weiser, has become malleable,
even ambivalent. What used to be sold to us as “authentic,” like the marketed
promise of a corporate brand, is now read as polysemic multiplicity.10
Google’s, Quantcast’s, and Alexa’s interpretations of my data are
necessarily contradictory because they each speak about me from their
own, proprietary scripts. Each is ambivalent about who I a