Kate Crawford

CRITICAL QUESTIONS FOR BIG DATA

The era of Big Data has begun. Computer scientists, physicists, economists, mathematicians, political scientists, bio-informaticists, sociologists, and other scholars are clamoring for access to the massive quantities of information produced by and about people, things, and their interactions. Diverse groups argue about the potential benefits and costs of analyzing genetic sequences, social media interactions, health records, phone logs, government records, and other digital traces left by people. Significant questions emerge. Will large-scale search data help us create better tools, services, and public goods? Or will it usher in a new wave of privacy incursions and invasive marketing? Will data analytics help us understand online communities and political movements? Or will it be used to track protesters and suppress speech? Will it transform how we study human communication and culture, or narrow the palette of research options and alter what ‘research’ means? Given the rise of Big Data as a socio-technical phenomenon, we argue that it is necessary to critically interrogate its assumptions and biases. In this article, we offer six provocations to spark conversations about the issues of Big Data: a cultural, technological, and scholarly phenomenon that rests on the interplay of technology, analysis, and mythology that provokes extensive utopian and dystopian rhetoric.

Following you: Disciplines of listening in social media

This paper develops the concept of listening as a metaphor for paying attention online. Pejorative terms such as ‘lurking’ have failed to capture much detail about the experience of presence online. Instead, much online media research has focused on ‘having a voice’, be it in blogs, wikis, social media, or discussion lists. The metaphor of listening can offer a productive way to analyse the forms of online engagement that have previously been overlooked, while also allowing a deeper consideration of the emerging disciplines of online attention. Social media are the focus of this paper, and in particular, how these platforms are changing the configurations of the ideal listening subject. Three modes of online listening are discussed: background listening, reciprocal listening, and delegated listening; Twitter provides a case study for how these modes are experienced and performed by individuals, politicians and corporations.

Six Provocations for Big Data

The era of Big Data has begun. Computer scientists, physicists, economists, mathematicians, political scientists, bio-informaticists, sociologists, and many others are clamoring for access to the massive quantities of information produced by and about people, things, and their interactions. Diverse groups argue about the potential benefits and costs of analyzing information from Twitter, Google, Verizon, 23andMe, Facebook, Wikipedia, and every space where large groups of people leave digital traces and deposit data. Significant questions emerge. Will large-scale analysis of DNA help cure diseases? Or will it usher in a new wave of medical inequality? Will data analytics help make people’s access to information more efficient and effective? Or will it be used to track protesters in the streets of major cities? Will it transform how we study human communication and culture, or narrow the palette of research options and alter what ‘research’ means? Some or all of the above?This essay offers six provocations that we hope can spark conversations about the issues of Big Data. Given the rise of Big Data as both a phenomenon and a methodological persuasion, we believe that it is time to start critically interrogating this phenomenon, its assumptions, and its biases.(This paper was presented at Oxford Internet Institute’s “A Decade in Internet Time: Symposium on the Dynamics of the Internet and Society” on September 21, 2011.)

Sexting, consent and young people's ethics: Beyond Megan's Story

This article contrasts the Megans Story campaign, a recent Australian media and policy response to sexting (the act of taking and transmitting naked or semi-naked pictures via mobile phones) with interview responses drawn from an Australian study that has asked young people about mobiles and sexting. It considers local and international responses to sexting as ‘child pornography,’ raising questions about the adequacy and appropriateness of criminalizing young peoples sexual self-representation and communication. Based on young peoples responses to sexting, the authors argue that there is an emerging ethics around the issue of consent being developed by young people. However, considerations of consent cannot be accounted for by the laws as they are presently framed, as under-18-year-olds currently are not allowed to consent to any form of sexting. This disconnection between the law and uses of technology by consenting teenagers generates problems both for policy, education and legal systems. This paper suggests a response that would recognize the seriousness of incidents of bullying, harassment or abuse, and would also take into account the meaning that sexting has for young people in specific contexts and cultures.

Our metrics, ourselves: A hundred years of self- tracking from the weight scale to the wrist wearable device

The recent proliferation of wearable self-tracking devices intended to regulate and measure the body has brought contingent questions of controlling, accessing and interpreting personal data. Given a socio-technical context in which individuals are no longer the most authoritative source on data about themselves, wearable self-tracking technologies reflect the simultaneous commodification and knowledge-making that occurs between data and bodies. In this article, we look specifically at wearable, self-tracking devices in order to set up an analytical comparison with a key historical predecessor, the weight scale. By taking two distinct cases of self-tracking – wearables and the weight scale – we can situate current discourses of big data within a historical framing of self-measurement and human subjectivity. While the advertising promises of both the weight scale and the wearable device emphasize self-knowledge and control through external measurement, the use of wearable data by multiple agents and institutions results in a lack of control over data by the user. In the production of self-knowledge, the wearable device is also making the user known to others, in a range of ways that can be both skewed and inaccurate. We look at the tensions surrounding these devices for questions of agency, practices of the body, and the use of wearable data by courtrooms and data science to enforce particular kinds of social and individual discipline.

What is a flag for? Social media reporting tools and the vocabulary of complaint

The flag is now a common mechanism for reporting offensive content to an online platform, and is used widely across most popular social media sites. It serves both as a solution to the problem of curating massive collections of user-generated content and as a rhetorical justification for platform owners when they decide to remove content. Flags are becoming a ubiquitous mechanism of governance—yet their meaning is anything but straightforward. In practice, the interactions between users, flags, algorithms, content moderators, and platforms are complex and highly strategic. Significantly, flags are asked to bear a great deal of weight, arbitrating both the relationship between users and platforms, and the negotiation around contentious public issues. In this essay, we unpack the working of the flag, consider alternatives that give greater emphasis to public deliberation, and consider the implications for online public discourse of this now commonplace yet rarely studied sociotechnical mechanism.

Departing glances: A sociotechnical account of ‘leaving’ Grindr

Grindr is a popular location-based social networking application for smartphones, predominantly used by gay men. This study investigates why users leave Grindr. Drawing on interviews with 16 men who stopped using Grindr, this article reports on the varied definitions of leaving, focusing on what people report leaving, how they leave and what they say leaving means to them. We argue that leaving is not a singular moment, but a process involving layered social and technical acts – that understandings of and departures from location-based media are bound up with an individual’s location. Accounts of leaving Grindr destabilize normative definitions of both ‘Grindr’ and ‘leaving’, exposing a set of relational possibilities and spatial arrangements within and around which people move. We conclude with implications for the study of non-use and technological departure.

Can an Algorithm be Agonistic? Ten Scenes from Life in Calculated Publics

This paper explores how political theory may help us map algorithmic logics against different visions of the political. Drawing on Chantal Mouffe’s theories of agonistic pluralism, this paper depicts algorithms in public life in ten distinct scenes, in order to ask the question, what kinds of politics do they instantiate? Algorithms are working within highly contested online spaces of public discourse, such as YouTube and Facebook, where incompatible perspectives coexist. Yet algorithms are designed to produce clear “winners” from information contests, often with little visibility or accountability for how those contests are designed. In isolation, many of these algorithms seem the opposite of agonistic: much of the complexity of search, ranking, and recommendation algorithms is nonnegotiable and kept far from view, inside an algorithmic “black box.” But what if we widen our perspective? This paper suggests agonistic pluralism as both a design ideal for engineers and a provocation to understand algorithms in a broader social context: rather than focusing on the calculations in isolation, we need to account for the spaces of contestation where they operate.

Where are human subjects in Big Data research? The emerging ethics divide:

There are growing discontinuities between the research practices of data science and established tools of research ethics regulation. Some of the core commitments of existing research ethics regulations, such as the distinction between research and practice, cannot be cleanly exported from biomedical research to data science research. Such discontinuities have led some data science practitioners and researchers to move toward rejecting ethics regulations outright. These shifts occur at the same time as a proposal for major revisions to the Common Rule—the primary regulation governing human-subjects research in the USA—is under consideration for the first time in decades. We contextualize these revisions in long-running complaints about regulation of social science research and argue data science should be understood as continuous with social sciences in this regard. The proposed regulations are more flexible and scalable to the methods of non-biomedical research, yet problematically largely exclude data science methods from human-subjects regulation, particularly uses of public datasets. The ethical frameworks for Big Data research are highly contested and in flux, and the potential harms of data science research are unpredictable. We examine several contentious cases of research harms in data science, including the 2014 Facebook emotional contagion study and the 2016 use of geographical data techniques to identify the pseudonymous artist Banksy. To address disputes about application of human-subjects research ethics in data science, critical data studies should offer a historically nuanced theory of “data subjectivity” responsive to the epistemic methods, harms and benefits of data science and commerce.

Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability:

Models for understanding and holding systems accountable have long rested upon ideals and logics of transparency. Being able to see a system is sometimes equated with being able to know how it works and govern it—a pattern that recurs in recent work about transparency and computational systems. But can “black boxes’ ever be opened, and if so, would that ever be sufficient? In this article, we critically interrogate the ideal of transparency, trace some of its roots in scientific and sociotechnical epistemological cultures, and present 10 limitations to its application. We specifically focus on the inadequacy of transparency for understanding and governing algorithmic systems and sketch an alternative typology of algorithmic accountability grounded in constructive engagements with the limitations of transparency ideals.