Professor Ignacio Cofone has published a new article, “The Privacy Paradox Is a Misnomer: Data Under Structural Uncertainty", in the Georgetown Law Technology Review (January 2026).
The article challenges the prevalent idea of a “privacy paradox”; the apparent inconsistency between people’s stated concern for privacy and their willingness to disclose personal information. This concept has played a prominent role in debates in privacy law and policy, giving rise to two largely separate bodies of literature: one offering experimental evidence of inconsistent behaviour, and another providing qualitative accounts defending the importance of privacy.
Professor Cofone’s article bridges these literatures through an online field experiment that reinterprets observed privacy behaviour. Rather than reflecting irrational or inconsistent decision-making, the findings show that disclosure choices are shaped by structural uncertainty about privacy risks. By isolating discounting mechanisms and empirically testing whether privacy choices reflect temptation or rational responses to uncertainty, the study demonstrates that such behaviour is consistent with decision-making under conditions of incomplete information.
The article goes on to examine the policy implications of this reframing. If privacy decisions stem from structural uncertainty (a form of market failure) regulation should aim to reduce that uncertainty. The findings support regulatory approaches that prioritise transparency, enabling individuals to better assess the risks associated with data collection, as well as flexible mechanisms that accommodate evolving technological and social contexts. This perspective also provides a new argument for the right to be forgotten, which allows individuals to revisit prior disclosures as new risks become apparent.
By shifting the focus away from individual inconsistency and towards the structural features of decision-making environments, the article contributes to ongoing debates about how privacy law should respond to contemporary data practices.