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 exploded the scope of personally identifiable information (“PII”). It has also effectively marginalized regulatory schema by evading current privacy protections with its novel methodology. Furthermore, poor execution of Big Data methodology may create additional harms by rendering inaccurate profiles that nonetheless impact an individual’s life and livelihood. To respond to Big Data’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 current privacy regimes offer limited nominal due process-like mechanisms, a more rigorous framework is needed to address their shortcomings. By examining 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 analogizes a system of regulation that would provide such rights against private Big Data actors.
Kate Crawford & Jason Schultz, Big Data and Due Process: Toward a Framework to Redress Predictive Privacy Harms, 55 B.C.L. Rev. 93 (2014), https://lawdigitalcommons.bc.edu/bclr/vol55/iss1/4