Advances in Algorithmic Recourse - Ensuring Causal Consistency, Fairness, and Robustness

Dive into a 42-minute conference talk from the Toronto Machine Learning Series where Assistant Professor Amir Hossein Karimi from the University of Waterloo examines the crucial intersection of causal inference and explainable AI for algorithmic recourse. Learn how causal consistency plays a vital role in addressing biases and promoting transparency in AI model decisions, with practical applications across healthcare, insurance, and banking sectors. Explore cutting-edge approaches for implementing fair and robust algorithmic solutions that ensure accountability and ethical decision-making in automated systems.