Unbiased Learning-to-Rank Needs Unconfounded Propensity Estimation

Explore a 15-minute conference presentation from ACM SIGIR 2024 that delves into the critical relationship between unbiased learning-to-rank systems and unconfounded propensity estimation. Learn from researchers Dan Luo, Lixin Zou, Qingyao Ai, Zhiyu Chen, Chenliang Li, Dawei Yin, and Brian Davison as they examine fairness considerations in ranking algorithms and demonstrate why accurate propensity estimation, free from confounding factors, is essential for creating truly unbiased learning-to-rank systems.