Fair Learning to Rank

Fair learning to rank (LTR) aims to develop ranking algorithms that are both accurate and unbiased, addressing the societal impact of biased ranking systems in applications like job searches and news feeds. Current research focuses on developing efficient and accurate models, including those based on ordered weighted averages and end-to-end optimization frameworks that incorporate fairness constraints directly into the learning process. A key challenge lies in handling the absence or inaccuracy of sensitive demographic data, with ongoing work exploring methods that are robust to noisy or inferred demographic information. These advancements are crucial for mitigating algorithmic bias and promoting equitable outcomes in various real-world applications.