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Block Selection

Written on 14.04.25 by Kavya Gupta

Dear Students, 
Welcome to Causethical ML 2.0 Seminar. smiley

As you might have noticed, the seminar is divided into 3 major themes/blocks.
As a first step to know you better and assign you better during the seminar, please select the preferences for the block you are interested in:
a) Block 1:… Read more

Dear Students, 
Welcome to Causethical ML 2.0 Seminar. smiley

As you might have noticed, the seminar is divided into 3 major themes/blocks.
As a first step to know you better and assign you better during the seminar, please select the preferences for the block you are interested in:
a) Block 1: Fairness and Causality
b) Block 2: Robustness and Causality
c) Block 3: Interpretability and Causality

For doing so, go to your personal status and under "tutorial preferences" and select your preference and click save.
Please don't use "Not possible" as a preference. 

Please do this by 21st April, 2025. 

Thank you. 

Best, 
Causethical ML Team

 

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.

The three main blocks are organized according to these core themes:

  • Block I: Fairness and Causality
  • Block II: Robustness and Causality
  • Block III: Interpretability and Causality

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 - 14:00.

Location:  SR 106 E1.1

Number of Students: 12

Attendance: Regular attendance is mandatory. Students may miss up to two sessions, for which they must be excused. In case of absence, students must still ensure they provide all deliverables by the due dates.

Seminar Schedule Overview

Timeline of Causethical seminar 2.0v

Dates

   Block  

Content

22/04/2025   Introduction to Seminar, Causality, Responsible AI
29/04/2025 Block I Introduction to Block I: Fairness and Causality
6/05/2025 Block I Student Presentation + QnA x2
13/05/2025 Block I Student Presentation + QnA x2
20/05/2025 Block I Panel Discussion
27/05/2025 Block II Introduction to Block II: Robustness and Causality
03/06/2025 Block II Student Presentation + QnA x2
10/06/2025 Block II Student Presentation + QnA x2
17/06/2025 Block II Panel Discussion
24/06/2025 Block III Introduction to Block III: Interpretability and Causality
01/07/2025 Block III Student Presentation + QnA x2
08/07/2025 Block III Student Presentation + QnA x2
15/07/2025 Block III Panel Discussion

 

Pool of papers : This selection of state-of-the-art papers represents what we believe are valuable resources for discussing the topics covered in this seminar.
Please select the block preference by 21/04/2025. The final decision of the block will be decided by TAs taking into account the preferences of the student. 

Presentation: The papers of the presentation will be given by TAs however you are free to supplement your presentation with more relevant articles. We always encourage addition of recent literature. 

 

Block I: Fairness and Causality

Block II: Robustness and Causality

Block III: Interpretability and Causality

 

 

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