PyTorch package to train and audit ML models for Individual Fairness

Code for the NeurIPS 2021 paper: Fairness in Ranking under Uncertainty

This project Implements the paper “Bridging Causality, Individual Fairness, and Adversarial Robustness in the Absence of Structural Causal Model” using the Python language.

Code accompanying ACM TORS paper "Relevance-aware Individual Item Fairness Measures for Recommender Systems: Limitations and Usage Guidelines" (Accepted in 2025)

DRO-FAIR: Distributionally Robust Optimization for joint Demographic Parity + Individual Fairness under adversarial data corruption (PGD/FGSM attacks, coordinated label flips). Implements Algorithm 1 from ICML submission with 150 experiments across Adult, Credit, and LSAC datasets.