Skip to Main Content
HBS Home
  • About
  • Academic Programs
  • Alumni
  • Faculty & Research
  • Baker Library
  • Giving
  • Harvard Business Review
  • Initiatives
  • News
  • Recruit
  • Map / Directions
Faculty & Research
  • Faculty
  • Research
  • Featured Topics
  • Academic Units
  • …→
  • Harvard Business School→
  • Faculty & Research→
Publications
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
ShareBar

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.
  • Read Now

About The Author

Himabindu Lakkaraju

Technology and Operations Management
→More Publications

More from the Authors

    • 2023
    • Faculty Research

    When Algorithms Explain Themselves: AI Adoption and Accuracy of Experts' Decisions

    By: Himabindu Lakkaraju and Chiara Farronato
    • 2022
    • Advances in Neural Information Processing Systems (NeurIPS)

    Which Explanation Should I Choose? A Function Approximation Perspective to Characterizing Post hoc Explanations

    By: Tessa Han, Suraj Srinivas and Himabindu Lakkaraju
    • 2022
    • Advances in Neural Information Processing Systems (NeurIPS)

    Efficiently Training Low-Curvature Neural Networks

    By: Suraj Srinivas, Kyle Matoba, Himabindu Lakkaraju and Francois Fleuret
More from the Authors
  • When Algorithms Explain Themselves: AI Adoption and Accuracy of Experts' Decisions By: Himabindu Lakkaraju and Chiara Farronato
  • Which Explanation Should I Choose? A Function Approximation Perspective to Characterizing Post hoc Explanations By: Tessa Han, Suraj Srinivas and Himabindu Lakkaraju
  • Efficiently Training Low-Curvature Neural Networks By: Suraj Srinivas, Kyle Matoba, Himabindu Lakkaraju and Francois Fleuret
ǁ
Campus Map
Harvard Business School
Soldiers Field
Boston, MA 02163
→Map & Directions
→More Contact Information
  • Make a Gift
  • Site Map
  • Jobs
  • Harvard University
  • Trademarks
  • Policies
  • Accessibility
  • Digital Accessibility
Copyright © President & Fellows of Harvard College