pith:Q2JDPCVE
Do Fair Models Reason Fairly? Counterfactual Explanation Consistency for Procedural Fairness in Credit Decisions
Outcome-fair credit models can still apply different reasoning to similar individuals, a hidden procedural bias missed by standard metrics.
arxiv:2605.12701 v1 · 2026-05-12 · cs.LG · cs.AI · cs.CE · cs.CY
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Claims
We show that existing outcome-fair models can still apply fundamentally different reasoning to individuals, a ``hidden procedural bias'' missed by standard fairness metrics and algorithms.
That nearest-neighbor counterfactuals and aligned integrated gradient attributions reliably capture and enforce procedural fairness without introducing artifacts from the generation method itself.
Outcome-fair credit models often exhibit hidden procedural bias through inconsistent reasoning across groups, which the CEC framework mitigates by enforcing consistent feature attributions via counterfactuals.
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| First computed | 2026-05-18T03:09:49.695868Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
8692378aa44eeb7d88549dc2c1b82d685991cafce3d6ebb012a3aeace6c69aa2
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/Q2JDPCVEJ3VX3CCUTXBMDOBNNB \
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Canonical record JSON
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