Artificial Intelligence Privacy and Convenience

The "Privacy and convenience vs big data" module serves as an introduction to the course, addressing critical topics such as data safety, the impact of algorithms on privacy, and adversarial attacks. Participants will gain valuable insights into real-world cases such as the Netflix Prize and Clearview AI, prompting critical thinking about privacy erosion and the implications for users.

The "Protecting Privacy: Theories and Methods" module dives deeper into the protection of privacy in machine learning projects. Participants will explore the concepts of noise versus signal, differential privacy, and the intersection of privacy and business interests. Additionally, they will examine real-world applications, including Apple's implementation of differential privacy.

In the "Building Transparent Models" module, participants will explore practical privacy methods and the dichotomy between glass box and black box models. They will also delve into the role of markets and game theory in empowering users, as well as the complexities of algorithmic decision-making. This module inspires critical thinking about trust and transparency in the context of predictive models.