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55 B.C. L. Rev. 93 (2014)
Big Data and Due Process: Toward a Framework to Redress Predictive Privacy Harms

handle is hein.journals/bclr55 and id is 93 raw text is: BIG DATA AND DUE PROCESS: TOWARD A FRAMEWORK TO REDRESS PREDICTIVE PRIVACY HARMS KATE CRAWFORD* JASON SCHULTZ** Abstract: The rise of Big Data analytics in the private sector poses new challenges for privacy advocates. Through its reliance on existing data and predictive analysis to create detailed individual profiles, Big Data has explod- ed the scope of personally identifiable information (PII). It has also effec- tively marginalized regulatory schema by evading current privacy protections with its novel methodology. Furthermore, poor execution of Big Data meth- odology may create additional harms by rendering inaccurate profiles that nonetheless impact an individual's life and livelihood. To respond to Big Da- ta's evolving practices, this Article examines several existing privacy regimes and explains why these approaches inadequately address current Big Data challenges. This Article then proposes a new approach to mitigating predictive privacy harms-that of a right to procedural data due process. Although cur- rent privacy regimes offer limited nominal due process-like mechanisms, a more rigorous framework is needed to address their shortcomings. By examin- ing due process's role in the Anglo-American legal system and building on previous scholarship about due process for public administrative computer systems, this Article argues that individuals affected by Big Data should have similar rights to those in the legal system with respect to how their personal data is used in such adjudications. Using these principles, this Article analo- gizes a system of regulation that would provide such rights against private Big Data actors. INTRODUCTION Big Data analytics have been widely publicized in recent years, with many in the business and science worlds focusing on how large datasets can © 2014, Kate Crawford and Jason Schultz. All rights reserved Principal Researcher, Microsoft Research; Visiting Professor, MIT Centre for Civic Media; Senior Fellow, NYU Infonnation Law Institute. ** Associate Professor of Clinical Law, NYU School of Law. The authors wish to thank Dan- ielle Citron, Michael Froomkin, Brian Pascal, Brian Covington, and the participants in the 2013 Privacy Law Scholars Conference for their valuable feedback. They also wish to thank Stephen Rushin, Ph.D. for his invaluable research assistance. 93

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