Graph Fairness via Authentic Counterfactuals: Tackling Structural and Causal Challenges
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it