Fair GLASSO: Estimating Fair Graphical Models with Unbiased Statistical Behavior

• URL: http://arxiv.org/abs/2406.09513v1
• Date: Thu, 13 Jun 2024 18:07:04 GMT
• Title: Fair GLASSO: Estimating Fair Graphical Models with Unbiased Statistical Behavior
• Authors: Madeline Navarro, Samuel Rey, Andrei Buciulea, Antonio G. Marques, Santiago Segarra,
• Abstract summary: Many real-world models exhibit unfair discriminatory behavior due to biases in data. We introduce fairness for graphical models in the form of two bias metrics to promote balance in statistical similarities. We present Fair GLASSO, a regularized graphical lasso approach to obtain sparse Gaussian precision matrices.
• Score: 31.92791228859847
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose estimating Gaussian graphical models (GGMs) that are fair with respect to sensitive nodal attributes. Many real-world models exhibit unfair discriminatory behavior due to biases in data. Such discrimination is known to be exacerbated when data is equipped with pairwise relationships encoded in a graph. Additionally, the effect of biased data on graphical models is largely underexplored. We thus introduce fairness for graphical models in the form of two bias metrics to promote balance in statistical similarities across nodal groups with different sensitive attributes. Leveraging these metrics, we present Fair GLASSO, a regularized graphical lasso approach to obtain sparse Gaussian precision matrices with unbiased statistical dependencies across groups. We also propose an efficient proximal gradient algorithm to obtain the estimates. Theoretically, we express the tradeoff between fair and accurate estimated precision matrices. Critically, this includes demonstrating when accuracy can be preserved in the presence of a fairness regularizer. On top of this, we study the complexity of Fair GLASSO and demonstrate that our algorithm enjoys a fast convergence rate. Our empirical validation includes synthetic and real-world simulations that illustrate the value and effectiveness of our proposed optimization problem and iterative algorithm.

Related papers

• Fairness-Aware Estimation of Graphical Models [13.39268712338485]
  This paper examines the issue of fairness in the estimation of graphical models (GMs) Standard GMs can result in biased outcomes, especially when the underlying data involves sensitive characteristics or protected groups. We introduce a comprehensive framework designed to reduce bias in the estimation of GMs related to protected attributes.
  arXiv  Detail & Related papers  (2024-08-30T16:30:00Z)
• Endowing Pre-trained Graph Models with Provable Fairness [49.8431177748876]
  We propose a novel adapter-tuning framework that endows pre-trained graph models with provable fairness (called GraphPAR) Specifically, we design a sensitive semantic augmenter on node representations, to extend the node representations with different sensitive attribute semantics for each node. With GraphPAR, we quantify whether the fairness of each node is provable, i.e., predictions are always fair within a certain range of sensitive attribute semantics.
  arXiv  Detail & Related papers  (2024-02-19T14:16:08Z)
• Joint Graph Learning and Model Fitting in Laplacian Regularized Stratified Models [5.933030735757292]
  Laplacian regularized stratified models (LRSM) are models that utilize the explicit or implicit network structure of the sub-problems. This paper shows the importance and sensitivity of graph weights in LRSM, and provably show that the sensitivity can be arbitrarily large. We propose a generic approach to jointly learn the graph while fitting the model parameters by solving a single optimization problem.
  arXiv  Detail & Related papers  (2023-05-04T06:06:29Z)
• Chasing Fairness Under Distribution Shift: A Model Weight Perturbation Approach [72.19525160912943]
  We first theoretically demonstrate the inherent connection between distribution shift, data perturbation, and model weight perturbation. We then analyze the sufficient conditions to guarantee fairness for the target dataset. Motivated by these sufficient conditions, we propose robust fairness regularization (RFR)
  arXiv  Detail & Related papers  (2023-03-06T17:19:23Z)
• Learning Graphical Factor Models with Riemannian Optimization [70.13748170371889]
  This paper proposes a flexible algorithmic framework for graph learning under low-rank structural constraints. The problem is expressed as penalized maximum likelihood estimation of an elliptical distribution. We leverage geometries of positive definite matrices and positive semi-definite matrices of fixed rank that are well suited to elliptical models.
  arXiv  Detail & Related papers  (2022-10-21T13:19:45Z)
• Evaluating State-of-the-Art Classification Models Against Bayes Optimality [106.50867011164584]
  We show that we can compute the exact Bayes error of generative models learned using normalizing flows. We use our approach to conduct a thorough investigation of state-of-the-art classification models.
  arXiv  Detail & Related papers  (2021-06-07T06:21:20Z)
• Learning Graphs from Smooth Signals under Moment Uncertainty [23.868075779606425]
  We consider the problem of inferring the graph structure from a given set of graph signals. Traditional graph learning models do not take this distributional uncertainty into account.
  arXiv  Detail & Related papers  (2021-05-12T06:47:34Z)
• Asymptotic Analysis of an Ensemble of Randomly Projected Linear Discriminants [94.46276668068327]
  In [1], an ensemble of randomly projected linear discriminants is used to classify datasets. We develop a consistent estimator of the misclassification probability as an alternative to the computationally-costly cross-validation estimator. We also demonstrate the use of our estimator for tuning the projection dimension on both real and synthetic data.
  arXiv  Detail & Related papers  (2020-04-17T12:47:04Z)
• Estimation of sparse Gaussian graphical models with hidden clustering structure [8.258451067861932]
  We propose a model to estimate the sparse Gaussian graphical models with hidden clustering structure. We develop a symmetric Gauss-Seidel based alternating direction method of the multipliers. Numerical experiments on both synthetic data and real data demonstrate the good performance of our model.
  arXiv  Detail & Related papers  (2020-04-17T08:43:31Z)
• Block-Approximated Exponential Random Graphs [77.4792558024487]
  An important challenge in the field of exponential random graphs (ERGs) is the fitting of non-trivial ERGs on large graphs. We propose an approximative framework to such non-trivial ERGs that result in dyadic independence (i.e., edge independent) distributions. Our methods are scalable to sparse graphs consisting of millions of nodes.
  arXiv  Detail & Related papers  (2020-02-14T11:42:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.