Unified Framework for Fair and Stable Graph Representation Learning

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. We establish a key connection between counterfactual fairness and stability and use it to develop a novel framework, NIFTY (uNIfying Fairness and stabiliTY), which can be used with any GNN to learn fair and stable representations.

We establish a key connection between counterfactual fairness and stability and leverage it to develop NIFTY (uNIfying Fairness and stabiliTY), a novel framework that 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.

Publication

Towards a Unified Framework for Fair and Stable Graph Representation Learning
Chirag Agarwal, Himabindu Lakkaraju*, Marinka Zitnik*
Conference on Uncertainty in Artificial Intelligence, UAI 2021 [arXiv] [poster]

@inproceedings{agarwal2021towards,
  title={Towards a Unified Framework for Fair and Stable Graph Representation Learning},
  author={Agarwal, Chirag and Lakkaraju, Himabindu and Zitnik, Marinka},
  booktitle={Proceedings of Conference on Uncertainty in Artificial Intelligence, UAI},
  year={2021}
}

Motivation

Over the past decade, there has been a surge of interest in leveraging GNNs for graph representation learning. GNNs have been used to learn powerful representations that enabled critical predictions in downstream applications—e.g., predicting protein-protein interactions, drug repurposing, crime forecasting, news and product recommendations.

As GNNs are increasingly implemented in real-world applications, it becomes important to ensure that these models and the resulting representations are safe and reliable. More specifically, it is important to ensure that:

  • these models and the representations they produce are not perpetrating undesirable discriminatory biases (i.e., they are fair), and
  • these models and the representations they produce are robust to attacks resulting from small perturbations to the graph structure and node attributes (i.e., they are stable).

NIFTY framework

We first identify a key connection between counterfactual fairness and stability. While stability accounts for robustness w.r.t. small random perturbations to node attributes and/or edges, counterfactual fairness accounts for robustness w.r.t. modifications of the sensitive attribute.

We leverage this connection to develop NIFTY that can be used with any existing GNN model to learn fair and stable representations. Our framework exploits the aforementioned connection to enforce fairness and stability both in the objective function as well as in the GNN architecture.

More specifically, we introduce a novel objective function which simultaneously optimizes for counterfactual fairness and stability by maximizing the similarity between representations of the original nodes in the graph, and their counterparts in the augmented graph. Nodes in the augmented graph are generated by slightly perturbing the original node attributes and edges or by considering counterfactuals of the original nodes where the value of the sensitive attribute is modified. We also develop a novel method for improving neural message passing by carrying out layer-wise weight normalization using the Lipschitz constant.

We theoretically show that this normalization promotes counterfactual fairness and stability of learned representations. To the best of our knowledge, this work is the first to tackle the problem of learning node representations that are both fair and stable.

The figure above gives an overview of NIFTY. NIFTY can learn node representations that are both fair and stable (i.e., invariant to the sensitive attribute value and perturbations to the graph structure and non-sensitive attributes) by maximizing the similarity between representations from diverse augmented graphs.

Datasets

We introduce and experiment with three new graph datasets comprising of critical decisions in criminal justice (if a defendant should be released on bail) and financial lending (if an individual should be given loan) domains.

  • German credit graph has 1,000 nodes representing clients in a German bank that are connected based on the similarity of their credit accounts. The task is to classify clients into good vs. bad credit risks considering clients’ gender as the sensitive attribute.
  • Recidivism graph has 18,876 nodes representing defendants who got released on bail at the U.S state courts during 1990-2009. Defendants are connected based on the similarity of past criminal records and demographics. The task is to classify defendants into bail (i.e., unlikely to commit a violent crime if released) vs. no bail (i.e., likely to commit a violent crime) considering race information as the protected attribute.
  • Credit defaulter graph has 30,000 nodes representing individuals that we connected based on the similarity of their spending and payment patterns. The task is to predict whether an individual will default on the credit card payment or not while considering age as the sensitive attribute.

Code

Source code is available in the GitHub repository.

Authors

Latest News

Oct 2021:   Adverse Drug Effects During the Pandemic

The COVID-19 pandemic has reshaped health and medicine in ways both dramatic and subtle. Some of the less obvious shifts can only emerge from analysis of millions of pieces of data—patient records, medical notes, clinical encounter reports. Check out the story in Harvard Medicine News highlighting our research.

Oct 2021:   Graph-Guided Networks for Time Series

New preprint! We introduce Raindrop, a graph-guided network for learning representations of irregularly sampled multivariate time series.

Oct 2021:   Massive Analysis of Differential Adverse Events

Hot off the press in Nature Computational Science! We develop an algorithmic approach for massive analysis of drug adverse events. Our analyses of 10,443,476 adverse event reports have implications for safe medication use and public health policy, and can enable comparison of COVID-19 pandemic to other health emergencies.

Sep 2021:   Leveraging Cell Ontology to Classify Cell Types

Hot off the press in Nature Communications! We developed OnClass, an algorithm and accompanying software for automatically classifying cells into cell types that are part of the controlled vocabulary that forms the Cell Ontology.

Sep 2021:   Major New Release of TDC

We are very excited to announce a major release of Therapeutics Data Commons! In the 0.3.0 release we restructured the codebase, simplified the backend and kept user interfaces the same. We also provide detailed documentation for our TDC package.

Aug 2021:   Trustworthy AI for Healthcare at AAAI

We will be organizing a meeting on Trustworthy AI for Healthcare at AAAI 2022. Stay tuned for details and call for papers.

Aug 2021:   New Paper on Therapeutics Data Commons

Our latest paper on Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development will appear at NeurIPS. We are excited to contribute novel datasets and benchmarks in the broad area of therapeutics.

Aug 2021:   AI for Science at NeurIPS

We are organizing the AI for Science workshop at NeurIPS 2021 and have a stellar lineup of invited speakers.

Aug 2021:   Best Poster Award at ICML Comp Biology

Congratulations to Michelle for winning the Best Poster Award for her work on deep contextual learners for protein networks at the ICML Workshop on Computational Biology.

Jul 2021:   Best Paper Award at ICML Interpretable ML

Our short paper on Interactive Visual Explanations for Deep Drug Repurposing received the Best Paper Award at the ICML Interpretable ML in Healthcare Workshop. Stay tuned for more news on this evolving project.

Jul 2021:   Five presentations at ICML 2021

Jun 2021:   Theory and Evaluation for Explanations

We introduce the first axiomatic framework for theoretically analyzing, evaluating, and comparing GNN explanation methods. We formalize key properties that all methods should satisfy to generate reliable explanations: faithfulness, stability, and fairness.

Jun 2021:   Deep Contextual Learners for Protein Networks

New preprint on contextualized protein embeddings aims to characterize genes with disease-specific interactions and elucidate disease manifestation in specific cell types.

May 2021:   New Paper Accepted at UAI

Our unified framework for fair and stable graph representation learning has just been accepted at UAI. We establish a theoretical connection between counterfactual fairness and stability and use it in a framework that can be used with any GNN to learn fair and stable embeddings.

Apr 2021:   Hot Off the Press: COVID-19 Repurposing in PNAS

Hot off the press! We deployed AI/ML and network medicine algorithms to rank 6,340 drugs for their expected efficacy against SARS-CoV-2. We screened in human cells the top-ranked drugs, identifying six drugs that reduced viral infection, four of which could be repurposed to treat COVID-19.

Apr 2021:   Representation Learning for Biomedical Nets

In our survey on representation learning for biomedical networks we discuss how long-standing principles of network biology and medicine provide the conceptual grounding for representation learning, explain its successes, and inform future advances.

Mar 2021:   Receiving Amazon Research Award

We are excited about receiving Amazon Faculty Research Award on Actionable Graph Learning for Finding Cures for Emerging Diseases. Thank you to Amazon Science for supporting our research.

Mar 2021:   Michelle's Graduate Research Fellowship

Michelle M. Li won the NSF Graduate Research Fellowship Award. Congratulations!

Mar 2021:   Hot Off the Press: Multiscale Interactome

Hot off the press! We develop a multiscale interactome approach to explain disease treatments. The approach can predict drug-disease treatments, identify proteins and biological functions related to treatment, and identify genes that alter treatment’s efficacy and adverse reactions.

Mar 2021:   Graph Networks in Computational Biology

We are excited to share slides from our recent lecture on Graph Neural Networks in Computational Biology, which we gave at Stanford ML for Graphs course.

Zitnik Lab  ·  Harvard  ·  Department of Biomedical Informatics