Pith Number

pith:OKMTJRGF

pith:2026:OKMTJRGFJ3DFEJELNJ76AE4JS7

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GESD: Beyond Outcome-Oriented Fairness

Gideon Popoola, John Sheppard

GESD measures fairness by tracking how consistently machine learning models explain their predictions across demographic subgroups.

arxiv:2605.15295 v1 · 2026-05-14 · cs.LG · cs.AI · cs.CY

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Record completeness

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3 Author claim open · sign in to claim

4 Citations open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

GESD effectively captures group-wise discrepancies in explanation quality, and that FEU improves both utility and fairness over state-of-the-art methods.

C2weakest assumption

That measuring disparities in explanation stability, robustness, and sensitivity across subgroups provides a meaningful and model-agnostic indicator of procedural fairness without requiring additional assumptions about the underlying explainer or data distribution.

C3one line summary

The paper proposes GESD, a procedural fairness metric for group disparities in explanation stability and robustness, and integrates it into the FEU multi-objective optimization framework.

References

29 extracted · 29 resolved · 1 Pith anchors

[1] Big data’s disparate impact, 2016

[2] Investigating and mitigating the performance–fairness tradeoff via protected-category sampling, 2024

[3] Optimized pre-processing for discrimination prevention 2017

[4] Fair prediction with disparate impact: A study of bias in recidivism prediction instruments, 2017

[5] Equality of opportunity in super- vised learning, 2016

Receipt and verification

First computed	2026-05-20T00:00:51.173372Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

729934c4c54ec652248b6a7fe0138997c749ff1a17cc3258d31df343839c6d20

Aliases

arxiv: 2605.15295 · arxiv_version: 2605.15295v1 · doi: 10.48550/arxiv.2605.15295 · pith_short_12: OKMTJRGFJ3DF · pith_short_16: OKMTJRGFJ3DFEJEL · pith_short_8: OKMTJRGF

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/OKMTJRGFJ3DFEJELNJ76AE4JS7 \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 729934c4c54ec652248b6a7fe0138997c749ff1a17cc3258d31df343839c6d20

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "b49f722b1fb25b77c7806681ec1c42166b2c3cef5e48fc58f42eb2bae1ed4243",
    "cross_cats_sorted": [
      "cs.AI",
      "cs.CY"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2026-05-14T18:10:57Z",
    "title_canon_sha256": "1f1f6439c864d6f2b289b47a2f866d75f99525d8e6587ee899bb6087e4be5dfc"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.15295",
    "kind": "arxiv",
    "version": 1
  }
}