Is invariance all you need for algorithmic fairness? Removing demographic information can create new bias
cs.LG, cs.AI
Submitted: 2026-09-25
Updated: 2026-09-25
Terminology
Sources
- Invariant Risk Minimization
- Fairness Under Group-Conditional Prior Probability Shift: Invariance, Drift, and Target-Aware Post-Processing
- 10 Years of Fair Representations: Challenges and Opportunities
- Path-Specific Counterfactual Fairness
- Rethinking Distributional Matching Based Domain Adaptation
- The Variational Fair Autoencoder
- Learning Adversarially Fair and Transferable Representations
- Towards Fairness under Label Bias in Image Segmentation: Impact, Measurement and Mitigation
- Are demographically invariant models and representations in medical imaging fair?
- On (assessing) the fairness of risk score models
- Stride-Net: Fairness-Aware Disentangled Representation Learning for Chest X-Ray Diagnosis
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