Artificial Intelligence Privacy and Convenience


In the "Artificial Intelligence Privacy and Convenience" course, participants will delve into fundamental concepts surrounding the security and privacy of machine learning projects. The curriculum emphasizes the ethical implications of decision-making in this domain and aims to equip learners with the knowledge and tools necessary to safeguard user privacy while developing effective predictive models.

The course is designed to provoke thought regarding the implementation of algorithms by businesses and their impact on user privacy and transparency both in the present and the future. Through a series of engaging modules, participants will gain insights into privacy theories and methods, explore the intricacies of building transparent models, and examine practical applications of privacy methods.

  • Explore fundamental concepts surrounding security and privacy in machine learning projects
  • Delve into the ethical implications of decision-making in this domain
  • Gain insights into privacy theories and methods
  • Examine practical applications of privacy methods

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Artificial Intelligence Privacy and Convenience
Course Modules

The "Artificial Intelligence Privacy and Convenience" course is divided into three modules: "Privacy and convenience vs big data," "Protecting Privacy: Theories and Methods," and "Building Transparent Models." Each module delves into essential aspects of security, privacy, and ethical decision-making in machine learning projects.

Privacy and convenience vs big data

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.

Protecting Privacy: Theories and Methods

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.

Building Transparent Models

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.

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