Research map / Learning theory and algorithms / Fairness
Fairness-Aware Estimation of Graphical Models
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Proposes Fair GMs (Gaussian, covariance, and Ising graphical models) by adding a pairwise graph disparity error to a nonsmooth multi-objective optimization solved with a proximal gradient method that has O(1/t) convergence guarantees.
Problem
Standard graphical model estimation (GLasso, covariance graphs, Ising models) can produce biased graphs that favor some sensitive groups over others when data are heterogeneous across groups.
Approach
Define a graph disparity error comparing a global graph's loss on each group with the group's optimal local graph, penalize pairwise differences between groups, and optimize this jointly with the GM loss as a nonsmooth multi-objective problem solved by a multi-objective proximal gradient method.
Key results
- On the simulated GLasso setting, Fair GLasso reduces the group PCEE difference from 0.0987 to 0.0569, and the summed disparity error falls by 92% with only -0.28% change in F1.
- On the Credit dataset, Fair CovGraph reduces disparity error by 53.76% at a cost of 0.42% in objective value.
- Fair GMs run substantially slower than standard GMs (e.g., 63.72s vs 8.47s on TCGA, 64.20s vs 0.26s on Credit).
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Replace standard GLasso with fairness-aware multi-objective optimization | GLasso · Simulation | Δ: 7.8149 -> 0.6237 | Helped |
| Replace standard CovGraph with fairness-aware multi-objective optimization | CovGraph · Simulation | Δ: 5.2627 -> 0.3889 | Helped |
| Replace standard BinNet with fairness-aware multi-objective optimization | BinNet · Simulation | Δ: 1$\times10^{-6}$ -> 0.0000 | Helped |
| Use fairness-aware multi-objective optimization | GLasso · TCGA Dataset | Δ: 2.5875 -> 0.0742 | Helped |
| Use fairness-aware multi-objective optimization | CovGraph · Credit Dataset | Δ: 0.5436 -> 0.2513 | Helped |
| Use fairness-aware multi-objective optimization | BinNet · LFM-1b Dataset | Δ: 0.0040 -> 0.0001 | Helped |
| Use fairness-aware multi-objective optimization | GLasso · Simulation (GLasso) | F1: 97.172 -> 97.443 | Hurt |
| Use fairness-aware multi-objective optimization | CovGraph · Simulation (CovGraph) | F1: 14.319 -> 14.484 | Hurt |
| Use fairness-aware multi-objective optimization | BinNet · Simulation (BinNet) | F1: 34.363 -> 34.362 | No clear effect |
| Use fairness-aware multi-objective optimization | GLasso · TCGA Dataset | F1: 127.96 -> 128.11 | Hurt |
| Use fairness-aware multi-objective optimization | CovGraph · Credit Dataset | F1: 9.2719 -> 9.3110 | Hurt |
| Use fairness-aware multi-objective optimization | BinNet · LFM-1b Dataset | F1: 87.531 -> 87.138 | Helped |
Surprising findings
- Standard BinNet's PCEE for Group 1 on the simulation was 0.4138 in the table but reported as 0.4444 in the text, an internal inconsistency.
- Fair GLasso on TCGA uniquely detected PIK3CD as a hub, which the authors interpret as sex-specific relevance but with limited direct evidence.
Limitations
- Fair GMs are substantially slower than standard GMs due to local graph computation and multi-objective optimization.
- Performance may be sensitive to the choice of loss function and balancing of multiple objectives.
- Theoretical assumptions (bounded level sets, convexity) and the authors note limitations for larger group sizes.
Experimental setup
Datasets: TCGA lung adenocarcinoma RNA-Seq ADNI AV45/AV1451 PET Credit datasets (UCI) LFM-1b synthetic Gaussian and Ising data
Benchmarks: Synthetic GLasso/CovGraph/BinNet TCGA Credit Dataset LFM-1b
Metrics: objective value F1 summed pairwise graph disparity error PCEE runtime
Baselines: Standard GLasso Standard CovGraph Standard BinNet ISTA
Builds on: GLasso ISTA Fair PCA multi-objective proximal gradient
Closest papers by meaning
- Fair GLASSO: Estimating Fair Graphical Models with Unbiased Statistical Behavior (NeurIPS 2024 · similarity 0.92)
- FairGP: A Scalable and Fair Graph Transformer Using Graph Partitioning (AAAI 2025 · similarity 0.87)
- FDGen: A Fairness-Aware Graph Generation Model (ICML 2025 · similarity 0.86)
- AIM-Fair: Advancing Algorithmic Fairness via Selectively Fine-Tuning Biased Models with Contextual Synthetic Data (CVPR 2025 · similarity 0.85)
- Fairness without Harm: An Influence-Guided Active Sampling Approach (NeurIPS 2024 · similarity 0.85)
- A Sequentially Fair Mechanism for Multiple Sensitive Attributes (AAAI 2024 · similarity 0.85)
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