Graph Fairness via Authentic Counterfactuals: Tackling Structural and Causal Challenges
Bibliographic record
Abstract
The extensive use of graph-based Machine Learning (ML) decision-making systems has raised numerous concerns about their potential discrimination, especially in domains with high societal impact. Various fair graph methods have thus been proposed, primarily relying on statistical fairness notions that emphasize sensitive attributes as a primary source of bias, leaving other sources of bias inadequately addressed. Existing works employ counterfactual fairness to tackle this issue from a causal perspective. However, these approaches suffer from two key limitations: they overlook hidden confounders that may affect node features and graph structure, leading to an oversimplification of causality and the inability to generate authentic counterfactual instances; they neglect graph structure bias, resulting in over-correlation of sensitive attributes with node representations. In response, this paper introduces the Authentic Graph Counterfactual Generator (AGCG), a novel framework designed to mitigate graph structure bias through a novel fair message-passing technique and to improve counterfactual sample generation by inferring hidden confounders. Comprising four key modules - subgraph selection, fair node aggregation, hidden confounder identification, and counterfactual instance generation - AGCG offers a holistic approach to advancing graph model fairness in multiple dimensions. Empirical studies conducted on both real and synthetic datasets demonstrate the effectiveness and utility of AGCG in promoting fair graph-based decision-making.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.082 | 0.262 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".