Multiverse Privacy Theory for Contextual Risks in Complex User-AI Interactions

TL;DR

This paper proposes Multiverse Privacy Theory, a new framework modeling privacy decisions as parallel universes to better understand complex, uncertain user-AI interactions and privacy outcomes.

Contribution

It introduces a novel multiverse-based framework for analyzing privacy decisions, incorporating contextual integrity and probabilistic modeling in complex AI interactions.

Findings

Framework models multiple potential privacy outcomes

Simulates privacy decision universes for better understanding

Lays groundwork for future empirical validation

Abstract

In an era of increasing interaction with artificial intelligence (AI), users face evolving privacy decisions shaped by complex, uncertain factors. This paper introduces Multiverse Privacy Theory, a novel framework in which each privacy decision spawns a parallel universe, representing a distinct potential outcome based on user choices over time. By simulating these universes, this theory provides a foundation for understanding privacy through the lens of contextual integrity, evolving preferences, and probabilistic decision-making. Future work will explore its application using real-world, scenario-based survey data.