Fairness Through Controlled (Un)Awareness in Node Embeddings
<|reference_start|>Fairness Through Controlled (Un)Awareness in Node Embeddings: Graph representation learning is central for the application of machine learning (ML) models to complex graphs, such as social networks. Ensuring `fair' representations is essential, due to the societal implications and the use of sensitive personal data. In this paper, we demonstrate how the parametrization of the \emph{CrossWalk} algorithm influences the ability to infer a sensitive attributes from node embeddings. By fine-tuning hyperparameters, we show that it is possible to either significantly enhance or obscure the detectability of these attributes. This functionality offers a valuable tool for improving the fairness of ML systems utilizing graph embeddings, making them adaptable to different fairness paradigms.<|reference_end|>
@article{vetter2024fairness,
    title={Fairness Through Controlled (Un)Awareness in Node Embeddings},
    author={Dennis Vetter and Jasper Forth and Gemma Roig and Holger Dell},
    journal={arXiv preprint arXiv:2407.20024},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.20024},
    primaryClass={cs.SI cs.CY}
}