Aligning Relational Learning with Lipschitz Fairness
Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics.
Keywords: Fairness Alignment, Relational Learning
Submission Guidelines: I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2024/AuthorGuide.
TL;DR: Discuss the behavior of the relational learning while input contains irrelevant factors, then align its output changes with Lipschitz fairness.
Abstract: Relational learning has gained significant attention, led by the expressiveness of Graph Neural Networks (GNNs) on graph data. While the inherent biases in common graph data are involved in GNN training, it poses a serious challenge to constraining the GNN output perturbations induced by input biases, thereby safeguarding fairness during training. The Lipschitz bound, a technique from robust statistics, can limit the maximum changes in the output concerning the input, taking into account associated irrelevant biased factors. It is an efficient and provable method to examine the output stability of machine learning models without incurring additional computational costs. Recently, its use in controlling the stability of Euclidean neural networks, the calculation of the precise Lipschitz bound remains elusive for non-Euclidean neural networks like GNNs, especially within fairness contexts. However, no existing research has investigated Lipschitz bounds to shed light on stabilizing the GNN outputs, especially when working on graph data with implicit biases. To narrow this gap, we begin with the general GNNs operating on relational data, and formulate a Lipschitz bound to limit the changes in the output regarding biases associated with the input. Additionally, we theoretically analyze how the Lipschitz bound of a GNN model could constrain the output perturbations induced by biases learned from data for fairness training. We experimentally validate the Lipschitz bound's effectiveness in limiting biases of the model output. Finally, from a training dynamics perspective, we demonstrate why the theoretical Lipschitz bound can effectively guide the GNN training to better trade-off between accuracy and fairness.
Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors' identity.
No Acknowledgement Section: I certify that there is no acknowledgement section in this submission for double blind review.
Primary Area: societal considerations including fairness, safety, privacy
Submission Number: 4063
Loading