Position: Personal Privacy Assistants Should Prompt User to Reflect

Personal privacy assistants (PPAs) are designed to efficiently automate privacy decisions based on user preferences. However, they may lead to cognitive offloading, leaving users unaware of privacy risks. We argue that alongside automatic controls, PPA should prompt user reflection. To operationalize this, we formalize human-agent interaction as a dual-objective optimization problem that balances system efficiency with active cognitive awareness. We provide step-wise modelings of this framework through a combination of Cooperative Inverse Reinforcement Learning (CIRL) and Partially Observable Markov Decision Process (POMDP). Additionally, we show specific cases of our framework's usage in different human-agent interactions. Finally, we discuss three open challenges in longitudinal interactions across the PPA, the human and their interactions.