Fairness-must-read-list

This repository collects:

1. Fairness Survey
2. Individual Fairness
3. Group Fairness
4. Counterfactual Fairness
5. Fairness on Graphs
6. Other Fairness Categories

More items will be added to the repository. Enjoy reading!

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Table of Contents

• 0. Fairness Survey
  • 0.1. A survey on bias and fairness in machine learning
  • 0.2. Fairness in deep learning: a computational perspective
• 1. Individual Fairness
  • 1.1. Fairness Through Awareness
  • 1.2. Learning Fair Representations
  • 1.3. iFair: Learning Individually Fair Data Representations for Algorithmic Decision Making
• 2. Group Fairness
  • 2.1. To be continued...
• 3. Counterfactual Fairness
  • 3.1. Counterfactual Fairness
• 4. Fairness on Graphs
  • 4.1. Individual Fairness
    • 4.1.1. Operationalizing Individual Fairness with Pairwise Fair Representations
    • 4.1.2. InFoRM: Individual Fairness on Graph Mining
  • 4.2. Group Fairness
    • 4.2.1. Fairwalk: Towards Fair Graph Embedding
    • 4.2.2. Compositional Fairness Constraints for Graph Embedding
    • 4.2.3. Bursting the Filter Bubble: Fairness-Aware Network Link Prediction
    • 4.2.4. DeBayes: a Bayesian method for debiasing network embeddings
    • 4.2.5. Fairness in Network Representation by Latent Structural Heterogeneity in Observational Data
    • 4.2.6. MONET: Debiasing Graph Embeddings via the Metadata-Orthogonal Training Unit
    • 4.2.7. FairGNN: Eliminating the Discrimination in Graph Neural Networks with Limited Sensitive Attribute Information
    • 4.2.8. (Fairness in Spectral Clustering) Guarantees for Spectral Clustering with Fairness Constraints
    • 4.2.9. (Information Fairness) On the Information Unfairness of Social Networks
  • 4.3. Other Fairness Categories
    • 4.3.1. (Fairness across node degree) Investigating and mitigating degree-related biases in graph convolutional networks