Six Provocations for Big Data

By D. boyd, Kate Crawford. 2011

587 citations recorded by the source corpus.

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.)

Subfields: Information Systems and Management, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/a14fcfd58e68f75cb619c72145576a70d6f1ce60

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Citations in the corpus

The nice thing about context is that everyone has it (2015, background): “In an earlier version of their paper, boyd and Crawford (2011) gave a different heading for their provocation on context: ‘Not all data are equivalent’.”
Digital Epidemiology (2012, background): “Some of the electronic traces that we leave as digital citizens are meant to be public, while others are not, resulting in ethical and legal challenges [33].”
Exploring Universal Patterns in Human Home-Work Commuting from Mobile Phone Data (2013, background): “Despite criticisms regarding the potential sampling biases of CDRs [10-11], to date CDRs remain as one of the most comprehensive and versatile data sources in helping us understand large-scale human mobility.”
Big other: surveillance capitalism and the prospects of an information civilization (2015, unknown): “The article builds on earlier warnings (e.g. boyd and Crawford, 2011; Bhimani and Willcocks, 2014) to sharply delineate the epistemic features of ‘ big data ’ – heterogeneous, unstructured, trans-semiotic, decontextualized, agnostic – and to illuminate the epistemological discontinuities such data…”
Fairness in Machine Learning: A Survey (2020, unknown): “However, fairness is a complex topic that has to try and balance many different contexts, multi-faceted sociocultural concepts (beyond race, gender, age, etc.), aspects of equality and diversity, and ethical principles such as whether it is even appropriate to use ML at all.”
Interactions with big data analytics (2012, background): “For instance, boyd and Crawford argue that working with big data is still subjective and that automated data collection is not self-explanatory— it requires selection and interpretation [4].”
Three maps and three misunderstandings: A digital mapping of climate diplomacy (2014, background): “Direct or reported evidence of such misunderstandings can, however, be found in literature (Barnes 2014; Bollier and Firestone, 2010; Boyd and Crawford, 2011; Carr, 2014; Cresswell, 2014; Dalton and Thatcher, the ﬁrst part of this article, we will address these three misunderstandings by describing…”
Protecting privacy when mining and sharing user data (2012, background): “Lack of ability to capitalize on the increased availability of online data by sharing and mining it across companies, institutions, and communities due to privacy concerns would be a loss to society and academic scholarship [30], significantly impeding innovation, and the ability of all members of s”
Big data, bigger dilemmas: A critical review (2015, background): “Big Data, boyd and Crawford (2011) posit, “is notable not because of its size, but because of its relationality to other data” (p. 1).”
This One Does Not Go Up to 11: The Quantified Self Movement as an Alternative Big Data Practice (2014, background): “Gillespie (2010), Manovich (2012), Pariser (2011), and boyd and Crawford (2011) all rightly complain that analyses of the way that algorithms shape society are frustrated by the inability to access those algorithms.”