Fall 2024 IDEAL Special Program on Interpretability, Privacy, and Fairness

Fall 2024

Fall 2024 IDEAL Special Program on Interpretability, Privacy, and Fairness

When working with data we typically want more than an accurate analysis.  We also want to understand why a model makes the recommendations it does, ensure it respects the privacy of individuals, and ensure that people are treated fairly.  The goal of this special program is to bring together researchers from computer science, economics, mathematics, electrical engineering, and statistics working on problems these challenges in a variety of contexts, including human-AI collaboration, large language models, and their role in the ethical use of AI. 

Organizers

• • Samir Khuller (Northwestern University)
  • Ian Kash (University of Illinois Chicago)
  • Mesrob Ohannessian (UIC)
  • Jessica Hullman (Northwestern University)
  • Binghui Wang (Illinois Institute of Technology)
  • Steven Keith Platt (Loyola University)
  • Diana Acosta Navas (Loyola University)
  • Liren Shan (TTIC)
  • Konstantin Makarychev (Northwestern University) 
  • Yury Makarychev (TTIC)
  • Gyorgy Turan (UIC)
  • Jason Hartline (Northwestern University) 
  • Yifan Wu (Northwestern University) 
  • Ren Wang (Illinois Institute of Technology)

Graduate Courses

Click here to visit IDEAL Fall 2024 Course Offerings
Form: https://forms.gle/fadznMognbKdcLBr9 

Weekly Reading Group

TBA