Reunion Lecture 2024. AI and Causality

Explore AI and Causality with Associate Professor of Computer Science, Elias Bareinboim.
Current AI systems are driven by data, often combined with probabilistic/statistical algorithms and other tools. However, statistical associations can’t always predict what will happen when environmental changes or external interventions occur. Systems must understand the often complex, dynamic, and unknown collection of causal mechanisms underlying the environment. This lack of understanding is undesirable because allowing AI to make decisions and influence society without comprehending the principles behind their choices is unscientific.
Bareinboim's research focuses on causal inference and its applications to artificial intelligence, machine learning, and data science, including in the health and social sciences.