Unbiased Learning

Unbiased learning aims to develop machine learning models that are not skewed by biases present in training data, ensuring fair and accurate predictions across different groups. Current research focuses on mitigating biases in various applications, including ranking, recommendation systems, and continual learning, employing techniques like inverse propensity scoring, doubly robust estimation, and adversarial learning within model architectures such as two-tower models and variational autoencoders. Addressing these biases is crucial for building trustworthy AI systems and ensuring equitable outcomes in diverse real-world applications, impacting fields ranging from search engines and recommender systems to medical diagnosis and social sciences.