Li Xiong

Large AI models such as ChatGPT are becoming new gateways to human knowledge. They are trained on vast collections of human-generated data, including books, websites, articles, images, and other records of social and cultural life. In doing so, they compress parts of our collective experience into computational representations that shape what appears visible, authoritative, or forgotten. This project examines these models as emerging forms of collective memory.

The project asks how AI systems “remember” information, how they “forget” it, and who has the power to decide what should be retained, removed, or emphasized. It focuses especially on the role of privacy technologies and governance mechanisms. Tools such as data filtering, differential privacy, machine unlearning, and model editing are designed to protect individuals and reduce harm, but they can also reshape what a model learns and what future users are able to access.

The central question is how to balance privacy, fidelity, and fairness. Strong privacy protections may reduce exposure of sensitive personal data, but they may also distort or erase information about smaller communities, rare events, or marginalized groups. On the other hand, high-fidelity models may preserve more detail but risk reproducing private, harmful, or biased information. By connecting technical developments in AI privacy with broader questions about memory, power, and representation, this project aims to offer a framework for understanding how AI systems are changing the way societies preserve and access knowledge.