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Publications
  • 2021
  • Chapter
  • Proceedings of the 37th Conference on Uncertainty in Artificial Intelligence

Towards a Unified Framework for Fair and Stable Graph Representation Learning

By: Chirag Agarwal, Himabindu Lakkaraju and Marinka Zitnik
  • Format:Electronic
  • | Pages:11
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Abstract

As the representations output by Graph Neural Networks (GNNs) are increasingly employed in real-world applications, it becomes important to ensure that these representations are fair and stable. In this work, we establish a key connection between counterfactual fairness and stability and leverage it to propose a novel framework, NIFTY (uNIfying Fairness and stabiliTY), which can be used with any GNN to learn fair and stable representations. We introduce a novel objective function that simultaneously accounts for fairness and stability and develop a layer-wise weight normalization using the Lipschitz constant to enhance neural message passing in GNNs. In doing so, we enforce fairness and stability both in the objective function as well as in the GNN architecture. Further, we show theoretically that our layer-wise weight normalization promotes counterfactual fairness and stability in the resulting representations. We introduce three new graph datasets comprising of high-stakes decisions in criminal justice and financial lending domains. Extensive experimentation with the above datasets demonstrates the efficacy of our framework.

Keywords

Graph Neural Networks; AI and Machine Learning; Prejudice and Bias

Citation

Agarwal, Chirag, Himabindu Lakkaraju, and Marinka Zitnik. "Towards a Unified Framework for Fair and Stable Graph Representation Learning." In Proceedings of the 37th Conference on Uncertainty in Artificial Intelligence, edited by Cassio de Campos and Marloes H. Maathuis, 2114–2124. AUAI Press, 2021.
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About The Author

Himabindu Lakkaraju

Technology and Operations Management
→More Publications

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    By: Sean McGrath, Parth Mehta, Alexandra Zytek, Isaac Lage and Himabindu Lakkaraju
    • 2023
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    Probabilistically Robust Recourse: Navigating the Trade-offs between Costs and Robustness in Algorithmic Recourse

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    On the Privacy Risks of Algorithmic Recourse

    By: Martin Pawelczyk, Himabindu Lakkaraju and Seth Neel
More from the Authors
  • When Does Uncertainty Matter? Understanding the Impact of Predictive Uncertainty in ML Assisted Decision Making By: Sean McGrath, Parth Mehta, Alexandra Zytek, Isaac Lage and Himabindu Lakkaraju
  • Probabilistically Robust Recourse: Navigating the Trade-offs between Costs and Robustness in Algorithmic Recourse By: Martin Pawelczyk, Teresa Datta, Johannes van-den-Heuvel, Gjergji Kasneci and Himabindu Lakkaraju
  • On the Privacy Risks of Algorithmic Recourse By: Martin Pawelczyk, Himabindu Lakkaraju and Seth Neel
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