DRAFT: PLEASE DO NOT CITE OR CIRCULATE Dear Culp Colloquium Reader: This is a very early draft. Many thanks in advance for your comments and recommendations for improvement. Best, Margaret CRITICAL DATA THEORY Margaret Hu† ABSTRACT This Article contends that the concept of big data must be subjected to critical theoretical treatment as a prerequisite to assessing its impact on newly emerging law and policy developments, and on core constitutional rights and principles. Specifically, the advent of the big data revolution necessitates an evolution of theoretical structures of analysis to properly critique the intersection of digital data, law, and power. Just as Critical Race Theory introduced an approach to legal jurisprudence to deconstruct race classification as it operates in legal contexts, Critical Data Theory is now required to accomplish similar aims with regards to big data at its intersection with the law. The tools of big data and data science allow for legal, scientific, and socioeconomic-political constructions that parallel the manner in which tools of race negotiation and race definition have facilitated legal, scientific, and socioeconomic-political constructions. Tools of indexation and theories of knowledge production, often presented as scientifically objective and epistemological in nature and, therefore, nondiscriminatory, can nonetheless shape the treatment of classes and subclasses of individuals in profoundly disparate ways. Consequently, this Article concludes that the legality and constitutionality of big data techniques of governance can be better understood through the application of analytical frameworks that assess emerging big data identity and big data policy structures through a critical theoretical lens. † Margaret Hu, Assistant Professor of Law, Washington and Lee University School of Law [Acknowledgments] DRAFT: PLEASE DO NOT CITE OR CIRCULATE 5/18/16 9:27 AM TABLE OF CONTENTS ABSTRACT ................................................................................................. 1 TABLE OF CONTENTS ................................................................................. 2 INTRODUCTION .......................................................................................... 2 I. BACKGROUND ON CRITICAL THEORY AND BIG DATA GOVERNANCE ................................................................................ 11 A. Critical Theory and Big Data ................................................ 17 B. Big Data Governance Revolution .......................................... 27 C. Criticality and Construction ................................................. 36 II. CRITICAL RACE THEORY AND BIG DATA .......................................... 41 A. The Critical Race Theory Framework ................................... 43 B. Disparate Impact of Big Data Tools and Programs ............... 47 C. Applying Critical Race Theory to Current Big Data Developments ........................................................................ 54 III. CRITICAL DATA THEORY ................................................................ 60 A. Emerging Big Data Techniques of Governance .................... 61 B. Applying Critical Data Theory to Big Data Developments ........................................................................ 68 CONCLUSION ............................................................................................ 75 INTRODUCTION 1 What is Critical Data Theory and why is it necessary? 1. Critical Data Theory is inspired by and borrows from Critical Race Theory. Critical Race Theory scholars have contributed significant works in recent years to develop how and why a theoretical approach to analyzing race in legal contexts is essential. See, e.g., DERRICK BELL, FACES AT THE BOTTOM OF THE WELL: THE PERMANENCE OF RACISM (1993); PATRICIA WILLIAMS, THE ALCHEMY OF RACE AND RIGHTS: DIARY OF A LAW PROFESSOR (1992); THE NEW BLACK: WHAT HAS CHANGED—AND WHAT HAS NOT—WITH RACE IN AMERICA (Kenneth W. Mack & Guy-Uriel Charles eds.) (2013); DEVON CARBADO & MITU GULATI, ACTING WHITE? RETHINKING RACE IN POST-RACIAL AMERICA (2013); RICHARD DELGADO & JEAN STEFANCIC, CRITICAL RACE THEORY: AN INTRODUCTION (2012); JESSIE DANIELS, CYBER RACISM: WHITE SUPREMACY ONLINE AND THE NEW ATTACKS ON CIVIL RIGHTS (2009); LANI GUINIER, LIFT EVERY VOICE: TURNING A CIVIL RIGHTS SETBACK INTO NEW VISION OF SOCIAL JUSTICE (2003); DOROTHY BROWN, CRITICAL RACE THEORY: CASES, MATERIALS, AND PROBLEMS (2014); Devon W. Carbado, Critical What What?, 43 CONN. L. REV. 1593 (2011). Derrick Bell, Racial Realism, 24 CONN. L. REV. 363 (1992); Guy-Uriel Charles, Colored Speech: Cross Burnings, Epistemics, and the Triumph of the Crits?, 93 GEO. L.J. 575 (2005); Kimberlé Crenshaw, Race, Reform and Retrenchment: Transformation and Legitimation in Antidiscrimination Law, 101 HARV. L. REV. 1331 (1988); Neil Gotanda, A Critique of “Our Constitution is Color-Blind,” 44 STAN. L. REV. 1 (1991); Jerry Kang, Trojan Horses of Race, 118 HARV. L. REV. 1489 (2005); Jerry Kang, Implicit Bias and the Pushback from the Left, 54 ST. LOUIS U. L.J. 1139 (2010); Mari Matsuda, When the First Quail Calls: Multiple Consciousness as Jurisprudential Method, DRAFT: PLEASE DO NOT CITE OR CIRCULATE 2016] CRITICAL DATA THEORY 3 This Article introduces Critical Data Theory as a theoretical lens by which to examine the legal and constitutional impact of newly emerging big data governance methods.2 As we transition from a small data3 world to a big data4 world, what we consider to be acceptable modes of acquiring knowledge and methods of assessing verifiable evidence to inform governmental decisionmaking is rapidly changing.5 So, too, logically, 14 WOMEN’S RTS. L. REP. 297 (1992); Trina Jones, Shades of Brown: the Law of Skin Color, 49 DUKE L. J. 1487 (2000); Paul Butler, Much Respect: Toward a Hip-Hop Theory of Punishment, 56 STAN. L. REV. 983 (2004); Paul Butler, By Any Means Necessary: Using Violence and Subversion to Change Unjust Law, 50 UCLA L. REV. 721 (2003); Devon Carbado, Critical Race Theory Meets Social Science, 10 ANN. REV. L. SOC. SCI. 149 (2014). 2 . See, e.g., Jack M. Balkin, Old-School/New-School Speech Regulation, 127 HARV. L. REV. 2296, 2297 (2014) (“The digital era is different. Governments can target for control or surveillance many different aspects of the digital infrastructure that people use to communicate: telecommunications and broadband companies, web hosting services, domain name registrars, search engines, social media platforms, payment systems, and advertisers.”); Jack M. Balkin, The Constitution in the National Surveillance State, 93 MINN. L. REV. 1 (2008) [hereinafter Balkin, National Surveillance State]; Jack M. Balkin & Sanford Levinson, The Processes of Constitutional Change: From Partisan Entrenchment to the National Surveillance State, 75 FORDHAM L. REV. 489 (2006); Lior Jacob Strahilevitz, Signaling Exhaustion and Perfect Exclusion, 10 J. ON TELECOMM. & HIGH TECH. L. 321 (2012); David Lyon, Biometrics, Identification and Surveillance, 22 BIOETHICS 499 (2008); Erin Murphy, Paradigms of Restraint, 57 DUKE L.J. 1321 (2008). 3. “‘Small data,’ like ‘big data,’ has no set definition.” Andrew Guthrie Ferguson, Big Data and Predictive Reasonable Suspicion, 163 U. PA. L. REV. 327, 329 n.6 (2015). “Small data” has been described in the following way: “Generally, small data is thought of as solving discrete questions with limited and structured data, and the data are generally controlled by one institution.” Id. (citing JULES J. BERMAN, PRINCIPLES OF BIG DATA: PREPARING, SHARING, AND ANALYZING COMPLEX INFORMATION 1–2 (2013)). 4. “Big data” is difficult to define, as it is a newly evolving field and the technologies that it encompasses are evolving rapidly as well. See, e.g., The Big Data Conundrum: How to Define It?, MIT TECH. REV. (Oct. 3, 2013), http://www.te chnologyreview.com/view/519851/the-big-data-conundrum-how-to-define-it/ (“In 2001, a Meta (now Gartner) report noted the increasing size of data, the increasing rate at which it is produced and the increasing range of formats and representations employed. This report predated the term ‘big data’ but proposed a three-fold definition encompassing the ‘three Vs’: Volume, Velocity and Variety. This idea has since become popular and sometimes includes a fourth V: veracity, to cover questions of trust and uncertainty.”). Neil M. Richards & Jonathan H. King, Big Data Ethics, 49 WAKE FOREST L. REV. 393, 394 n.3 (2014) (quoting IT Glossary: Big Data, GARTNER, http://www.gartner.com/it-glossary/big- data/ (last visited May 11, 2015)); see id. (citing the original “3-V” big data report, DOUG LANEY, 3D DATA MANAGEMENT: CONTROLLING DATA VOLUME, VELOCITY, AND VARIETY (2001), http://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management- Controlling-Data-V olume-Velocity-and-Variety.pdf). Multiple authors have addressed the characteristics of “big data” and the challenges posed by big data technologies. See, e.g., VIKTOR MAYER-SCHÖNBERGER & KENNETH CUKIER, BIG DATA: A REVOLUTION THAT WILL TRANSFORM HOW WE LIVE, WORK, AND THINK (2013); PRIVACY, BIG DATA, AND THE PUBLIC GOOD: FRAMEWORKS FOR ENGAGEMENT (Julia Lane, Victoria Stodden, Stefan Bender & Helen Nissenbaum eds., 2014); ROB KITCHIN, THE DATA REVOLUTION: BIG DATA, OPEN DATA, DATA INFRASTRUCTURES & THEIR CONSEQUENCES (2014). 5. danah boyd & Kate Crawford, Critical Questions for Big Data: Provocations for a Cultural, Technological, and Scholarly Phenomenon, 15 INFO., COMM. & SOC’Y 662, 662– DRAFT: PLEASE DO NOT CITE OR CIRCULATE