Fair Classification with Partial Feedback - An Exploration-Based Data Collection Approach

Learn about an innovative exploration-based data collection approach for fair classification with partial feedback in this 23-minute ACM conference talk. Explore how researchers Vijay Keswani, Anay Mehrotra, and L. Elisa Celis tackle the challenges of building fair classification systems when dealing with incomplete or partial feedback data. Discover practical methodologies for collecting and utilizing data in a way that promotes fairness while addressing the limitations of partial information in machine learning applications.