Navigating the Tradeoff Between Privacy and Fairness in Machine Learning

Explore the complex relationship between privacy and fairness in machine learning in this 33-minute conference talk from the Toronto Machine Learning Series. Delve into the challenges faced when deploying machine learning models in highly regulated industries, where data privacy and fairness are paramount concerns. Learn how privacy-enhancing technologies can inadvertently exacerbate unfair tendencies in models. Discover research-based solutions for navigating the delicate balance between protecting individual privacy and ensuring equitable outcomes. Gain valuable insights from Jesse Cresswell, Senior Machine Learning Scientist at Layer 6 AI, as he addresses the critical intersection of privacy and fairness in the evolving landscape of machine learning applications across society.

Navigating the Tradeoff Between Privacy and Fairness in ML