Vanderbilt Law Review

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In Herring v. United States, Chief Justice John Roberts reframed the Supreme Court's understanding of the exclusionary rule: "As laid out in our cases, the exclusionary rule serves to deter deliberate, reckless, or grossly negligent conduct, or in some circumstances recurring or systemic negligence." The open question remains: How can defendants demonstrate sufficient recurring or systemic negligence to warrant exclusion? The Supreme Court has never answered the question, although the absence of systemic or recurring problems has figured prominently in two recent exclusionary rule decisions. Without the ability to document recurring failures or patterns of police misconduct, courts can dismiss individual constitutional violations merely as examples of "isolated negligence."

But what if new data-driven surveillance technologies could track police-citizen interactions and uncover recurring or systemic problems? What if stops and arrests could be data mined to reveal systemic racial bias? What if new surveillance technologies could record police-citizen stops to monitor patterns of unconstitutional practices? What if predictive analytics could identify at-risk officers in order to predict future misconduct?

This Article looks to invert the big data surveillance gaze from the citizen to the police. It asks whether the same big data policing technologies built to track movements, actions, and patterns of criminal activity could be redesigned to foster data-driven police accountability. Tracking this "blue data" and studying the systemic errors offers concrete answers to the open questions surrounding the Supreme Court's new exclusionary rule.

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