Workshop on Decoding Decisions: Explainability in ML & Sequential Decision Making

Workshop at The Conference on Robots and Vision (CRV) 2025

Machine learning and sequential decision making systems are increasingly deployed in high-stakes domains such as healthcare, finance, and autonomous systems. However, the opaque nature of these models raises concerns about transparency, accountability, and robustness. This workshop aims to advance research in explainability for machine learning and sequential decision-making by exploring methodologies to interpret model-driven decisions, understand causal mechanisms, and ensure fairness and reliability in real-world use cases.

Submission Deadline: 18th May, 2025, 11:59 PM (AoE).
The workshop will be held on 26th May in Calgary.
For latest news about the workshop, follow @ddxmlcrv on X/Twitter.


Confirmed Speakers

David Meger
David Meger

Associate Professor

McGill University, Mila

Fairness in Reinforcement Learning with Bisimulation Metrics

Svetlana Yanushkevich
Svetlana Yanushkevich

Professor

University of Calgary

LLMs for Expert Elicitation in Probabilistic Causal Modeling

Hassan Sajjad
Hassan Sajjad

Associate Professor

Dalhousie University

Latent Concept-Based Explanation of NLP Models

Tutorials

Samira Ebrahimi Kahou
Samira Ebrahimi Kahou

Assistant Professor

University of Calgary, Mila, CIFAR AI Chair

Explainability in Machine Learning

Ulrich Aïvodji
Ulrich Aïvodji

Assistant Professor

ETS Montreal, Mila

Overview of Interpretability and Explainability Methods, and Practical Considerations

Organizers

Ankur Garg

University of Calgary
 
 

Manizheh GhaemiDizaji

University of Calgary
 
 

Rishav Rishav

Mila, University of Calgary
 
 

Samira Ebahimi Kahou

University of Calgary, Mila, CIFAR AI Chair
 
 

Program Committee

  • Ankur Garg
  • Manizheh GhaemiDizaji
  • Rishav Rishav
  • Kiana Kazeminejad
  • Gona Rahmaniani

Questions?

Contact us at explainableml2025@gmail.com or @ddxmlcrv.