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pith:2026:Q2JDPCVEJ3VX3CCUTXBMDOBNNB
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Do Fair Models Reason Fairly? Counterfactual Explanation Consistency for Procedural Fairness in Credit Decisions

Gideon Popoola, John Sheppard

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

C1strongest claim

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.

C2weakest assumption

That nearest-neighbor counterfactuals and aligned integrated gradient attributions reliably capture and enforce procedural fairness without introducing artifacts from the generation method itself.

C3one line summary

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.

References

299 extracted · 299 resolved · 5 Pith anchors

[1] Women in the American Political System: An Encyclopedia of Women as Voters, Candidates, and Office Holders , volume=
[2] Home Mortgage Disclosure Act, and Community , year=
[3] Causality , author=. 2009 , publisher= 2009
[4] International Journal of Management , volume=
[5] Applied Soft Computing , volume= 2020

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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

Aliases

arxiv: 2605.12701 · arxiv_version: 2605.12701v1 · doi: 10.48550/arxiv.2605.12701 · pith_short_12: Q2JDPCVEJ3VX · pith_short_16: Q2JDPCVEJ3VX3CCU · pith_short_8: Q2JDPCVE
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Canonical record JSON
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