ABSTRACT
This essay offers a theoretical account of one reason that current privacy regulation fails. I argue that existing privacy laws inherit a focus on judicial subjects, using language about torts and abstract rights. Current threats to privacy, on the other hand, presuppose statistically-generated populations and aggregative models of subjectivity. This gap in underlying presupposition generates the need to translate privacy protections into the data era. After situating my theoretical account, I identify two areas where data subjects face entry barriers to the legal protections afforded data subjects. On the one hand, data subjects are often denied standing because privacy harms are deemed speculative and abstract. On the other hand, algorithmic governance makes it hard to argue that one is an exception to a rule, depriving data subjects of traditional equity. I then offer two ways to translate privacy for data subjects. The first is reform of standing through reformed class certification, making the harms data subjects face as populations judicially legible. The second is a reconceptualization of equity, reframing it both to re-establish the right to be an exception to a data-driven rule and to allow equitable consideration of issues that face statistical populations.
Hull, Gordon, Translating Privacy for Data Subjects (January 30, 2025).
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