Causethical Machine Learning

Causethical Machine Learning 2.0

Now in its second iteration, this seminar explores the intersection of causality with robustness, interpretability, and fairness in machine learning, exploring how causal reasoning can help build more reliable, explainable, and fair AI systems. As AI increasingly influences critical domains such as healthcare, finance, and policy, ensuring that models make sound, transparent, and justifiable decisions is more important than ever. This seminar examines how causal methods provide a principled approach to addressing key challenges in AI, moving beyond correlations to uncover true cause-and-effect relationships.

Structured into three core themes : Robustness, Interpretability, and Fairness the seminar engages participants in an in-depth exploration of causal theory, methodological innovations, and real-world applications. Through a carefully curated selection of foundational and state-of-the-art research, students will analyze how causality enhances model reliability, improves decision transparency, and mitigates bias in AI systems.

Each session will feature discussions on recent research, student-led paper presentations, and critical engagement with open problems in the field. By blending theoretical insights with practical motivation, this seminar provides students with a deep, structured understanding of how causal methods contribute to the development of Responsible and Ethical AI.

 

Date and time:  Weekly. Tuesday 12:30 PM - 2PM.

Location:  TBD

Number of Students: 12