ICML 2019oral328 citations
Compositional Fairness Constraints for Graph Embeddings
Avishek Bose, William Hamilton
Abstract
Learning high-quality node embeddings is a key building block for machine learning models that operate on graph data, such as social networks and recommender systems. However, existing graph embedding techniques are unable to cope with fairness constraints, e.g., ensuring that the learned representations do not correlate with certain attributes, such as age or gender. Here, we introduce an adversarial framework to enforce fairness constraints on graph embeddings. Our approach is
BibTeX
@InProceedings{pmlr-v97-bose19a,
title = {Compositional Fairness Constraints for Graph Embeddings},
author = {Bose, Avishek and Hamilton, William},
booktitle = {Proceedings of the 36th International Conference on Machine Learning},
pages = {715--724},
year = {2019},
editor = {Chaudhuri, Kamalika and Salakhutdinov, Ruslan},
volume = {97},
series = {Proceedings of Machine Learning Research},
month = {09--15 Jun},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v97/bose19a/bose19a.pdf},
url = {https://proceedings.mlr.press/v97/bose19a.html},
abstract = {Learning high-quality node embeddings is a key building block for machine learning models that operate on graph data, such as social networks and recommender systems. However, existing graph embedding techniques are unable to cope with fairness constraints, e.g., ensuring that the learned representations do not correlate with certain attributes, such as age or gender. Here, we introduce an adversarial framework to enforce fairness constraints on graph embeddings. Our approach is