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FairSIN: Achieving Fairness in Graph Neural Networks through Sensitive Information Neutralization

Cheng Yang, Jixi Liu, Yunhe Yan, Chuan Shi

TL;DR

FairSIN addresses bias in graph neural networks by replacing standard filtering-based debiasing with a neutralization-based paradigm that injects Fairness-facilitating Features (F3) derived from heterogeneous neighbor information. The approach has both data-centric and model-centric variants, supported by a theoretical framework that uses a generative process and a leakage metric $\mathcal{H}(s|x)$ to show how message passing can amplify bias and how F3 mitigates it. Empirically, FairSIN improves fairness metrics such as Demographic Parity and Equal Opportunity across five real-world datasets and three GNN backbones while maintaining or enhancing predictive accuracy. The results demonstrate a scalable, task-irrelevant pre-processing option (FairSIN-F) and a cohesive model-aware strategy (FairSIN) with an adversarial component, offering a practical path toward fairer graph learning in diverse domains.

Abstract

Despite the remarkable success of graph neural networks (GNNs) in modeling graph-structured data, like other machine learning models, GNNs are also susceptible to making biased predictions based on sensitive attributes, such as race and gender. For fairness consideration, recent state-of-the-art (SOTA) methods propose to filter out sensitive information from inputs or representations, e.g., edge dropping or feature masking. However, we argue that such filtering-based strategies may also filter out some non-sensitive feature information, leading to a sub-optimal trade-off between predictive performance and fairness. To address this issue, we unveil an innovative neutralization-based paradigm, where additional Fairness-facilitating Features (F3) are incorporated into node features or representations before message passing. The F3 are expected to statistically neutralize the sensitive bias in node representations and provide additional nonsensitive information. We also provide theoretical explanations for our rationale, concluding that F3 can be realized by emphasizing the features of each node's heterogeneous neighbors (neighbors with different sensitive attributes). We name our method as FairSIN, and present three implementation variants from both data-centric and model-centric perspectives. Experimental results on five benchmark datasets with three different GNN backbones show that FairSIN significantly improves fairness metrics while maintaining high prediction accuracies.

FairSIN: Achieving Fairness in Graph Neural Networks through Sensitive Information Neutralization

TL;DR

FairSIN addresses bias in graph neural networks by replacing standard filtering-based debiasing with a neutralization-based paradigm that injects Fairness-facilitating Features (F3) derived from heterogeneous neighbor information. The approach has both data-centric and model-centric variants, supported by a theoretical framework that uses a generative process and a leakage metric to show how message passing can amplify bias and how F3 mitigates it. Empirically, FairSIN improves fairness metrics such as Demographic Parity and Equal Opportunity across five real-world datasets and three GNN backbones while maintaining or enhancing predictive accuracy. The results demonstrate a scalable, task-irrelevant pre-processing option (FairSIN-F) and a cohesive model-aware strategy (FairSIN) with an adversarial component, offering a practical path toward fairer graph learning in diverse domains.

Abstract

Despite the remarkable success of graph neural networks (GNNs) in modeling graph-structured data, like other machine learning models, GNNs are also susceptible to making biased predictions based on sensitive attributes, such as race and gender. For fairness consideration, recent state-of-the-art (SOTA) methods propose to filter out sensitive information from inputs or representations, e.g., edge dropping or feature masking. However, we argue that such filtering-based strategies may also filter out some non-sensitive feature information, leading to a sub-optimal trade-off between predictive performance and fairness. To address this issue, we unveil an innovative neutralization-based paradigm, where additional Fairness-facilitating Features (F3) are incorporated into node features or representations before message passing. The F3 are expected to statistically neutralize the sensitive bias in node representations and provide additional nonsensitive information. We also provide theoretical explanations for our rationale, concluding that F3 can be realized by emphasizing the features of each node's heterogeneous neighbors (neighbors with different sensitive attributes). We name our method as FairSIN, and present three implementation variants from both data-centric and model-centric perspectives. Experimental results on five benchmark datasets with three different GNN backbones show that FairSIN significantly improves fairness metrics while maintaining high prediction accuracies.
Paper Structure (33 sections, 1 theorem, 6 equations, 5 figures, 2 tables)

This paper contains 33 sections, 1 theorem, 6 equations, 5 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Graph Neural Networks.
  4. Fairness in Graph Neural Networks.
  5. Methodology
  6. Preliminaries
  7. Notations.
  8. Task Definition.
  9. Theoretical Analysis
  10. Generative Process.
  11. Quantifying Sensitive Information Leakage.
  12. Message Passing Can Exacerbate Sensitive Biases.
  13. Summary.
  14. Implementations of FairSIN
  15. Data-centric Variants.
  16. ...and 18 more sections

Key Result

Theorem 1

Assume that node representations are biased and can be identified by the predictor, i.e.,$\mu_c > \mu_{ic}$. For node $v_i$, we consider a message passing process that updates $x_i$ by $x_i'=x_i+x_{i}^\textit{neigh}$. Then we have which means that the predictor $\hat{P}_\theta$ can identify the sensitive attributes more accurately.

Figures (5)

  • Figure 1: Motivation verification on Pokec datasets. Compared with vanilla GNN without fairness consideration, filtering-based methods, either edge-dropping nifty or feature-masking fairv, always have a trade-off between accuracy (ACC$\uparrow$) and fairness (DP$\downarrow$). While our method can improve both.
  • Figure 2: Motivation illustration of sensitive information neutralization. Here we assume binary sensitive groups denoted by +/-, and the numbers of +/- indicate the intensity of sensitive information leakage in node representations. (a) Message passing computation will aggregate both non-sensitive feature information (dot symbols) and sensitive biases (+/- symbols); (b) Current SOTA methods are usually filtering-based (e.g., edge dropping or feature masking), which may lose much non-sensitive information; (c) Our proposed neutralization-based strategy introduces F3 to statistically neutralize the sensitive bias and provide extra non-sensitive information.
  • Figure 3: Sensitive biases in four groups of features. The biases are measured by average $\hat{P}_\theta(s|x)$, and larger scores indicate more serious sensitive leakage in the representations.
  • Figure 4: Classification performance and group fairness under different values of hyper-parameter $\delta$.
  • Figure 5: Training time cost on Bail and Credit with GCN backbone (in seconds).

Theorems & Definitions (1)

  • Theorem 1