Using causality for model explanations and algorithmic fairness (49:16)

In this talk, Joshua will explore the intersection of causal modeling, fairness, and interpretability in machine learning. As machine learning models are increasingly used to make critical decisions in areas such as healthcare, criminal justice, and finance, ensuring that these models are both fair and understandable becomes paramount. Dr. Loftus discussed how causal models can be leveraged to identify and mitigate biases, ensuring that machine learning systems produce equitable outcomes.