From Defense to Advocacy: Empowering Users to Leverage the Blind Spot of AI Inference
Yumou Wei, John Carney, John Stamper, Nancy Belmont

TL;DR
This paper advocates shifting from passive privacy defenses to proactive user empowerment by developing personal agents that manage AI inference blind spots, transforming unknown inferences into user assets.
Contribution
It introduces a novel paradigm of privacy advocacy using personal agents to operationalize social norms and manage AI inference blind spots, addressing regulatory gaps.
Findings
Current regulations focus on data collection, not inference.
Personal advocacy agents can reveal and control AI inferences.
Transforming unknown inferences into assets benefits users.
Abstract
Most privacy regulations function as a passive defensive shield that users must wield themselves. Users are incessantly asked to "opt-in" or "opt-out" of data collection, forced to make defensive decisions whose consequences are increasingly difficult to predict. Viewed through the Johari Window, a psychological framework of self-awareness based on what is known and unknown to self and others, current policies require users to manage the Open Self and shield the Hidden Self through notice and consent. However, as organizations increasingly use AI to make inferences, the rapid expansion of Blind Self, attributes known to algorithms but unknown to the user, emerges as a critical challenge. We illustrate how current regulations fall short because they focus on data collection rather than inference and leave this blind spot unguarded. Building on the theory of Contextual Integrity, we…
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Taxonomy
TopicsEthics and Social Impacts of AI · Privacy, Security, and Data Protection · Innovative Human-Technology Interaction
