Table 1.

Framework for Analyzing Big Data Surveillance across Institutional Contexts

	Goals		Means	Ends
Types of Surveillance	Institutional Field	Relationship between Individual and Institution	Shifts in Surveillance Practices Associated with Big Data	Institutional Interventions	Consequences for Inequality
Categorical Suspicion	Criminal justice, intelligence	Classifying individuals according to risk; potential as criminals/ terrorists	1) Discretionary to quantified risk assessment 2) Explanatory to predictive analytics	Marking, apprehension, social control	Stigma, spillover into other institutions
Categorical Seduction	Finance, marketing, credit	Classifying individuals according to their value to companies; potential as customers	3) Query-based to alert-based systems 4)Moderate to low inclusion thresholds 5) Disparate to integrated data	Different products, perks, access to credit, opportunities, constraints	Upward or downward economic mobility; reproducing current patterns
Categorical Care	Medical care, public assistance	Classifying individuals according to their need; potential as clients		Personalized medicine, welfarist service delivery	May reduce inequality except when intersects with suspicion or seduction