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Block SelectionWritten on 14.04.25 by Kavya Gupta Dear Students, As you might have noticed, the seminar is divided into 3 major themes/blocks. Dear Students, As you might have noticed, the seminar is divided into 3 major themes/blocks. For doing so, go to your personal status and under "tutorial preferences" and select your preference and click save. Please do this by 21st April, 2025. |
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
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
- Avoiding Discrimination through Causal Reasoning
- Counterfactual Fairness
- Fairness-Accuracy Trade-Offs: A Causal Perspective
- Counterfactual Fairness Is Basically Demographic Parity
- Promises and Challenges of Causality for Ethical Machine Learning
- Counterfactual Fairness Is Not Demographic Parity, and Other Observations
- Causal Conceptions of Fairness and their Consequences
Block II: Robustness and Causality
- Causality-Inspired Representation Learning for Domain Generalization
- Learning causal representations for robust domain adaptation
- Invariant Causal Representation Learning for Out-of-Distribution Generalization
- Learning Causally Disentangled Representations via the Principle of Independent Causal Mechanisms
- Robust Causal Graph Representation Learning against Confounding Effects
- Representation Learning via Invariant Causal Mechanisms
Block III: Interpretability and Causality
- Interpretability is in the mind of the beholder: A causal framework for human-interpretable representation learning
- Explaining Classifiers with Causal Concept Effect
- Causal Concept Graph Models: Beyond Causal Opacity in Deep Learning
- From Causal to Concept-Based Representation Learning
- Causally Reliable Concept Bottleneck Models