Paper Citation Record · LEDGER

Explainability for fair machine learning

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2010.07389.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2010.07389 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T04:37:34.439799Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-21T11:30:02.491090Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 6dc0ae68-7bed-409e-8408-5776765e0054 · inbound

Constructing Fair Latent Space for Intersection of Fairness and Explainability cites this paper.

Constructing Fair Latent Space for Intersection of Fairness and Explainability Explainability for fair machine learning

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-11T05:33:11.869382Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1486d5c1-3060-4dbe-a4e0-24e4b76985f1 · inbound

Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data cites this paper.

Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data Explainability for fair machine learning

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-10T15:39:37.394940Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 78e4262d-591c-44de-b500-72269fe1560a · inbound

Explanations as Bias Detectors: A Critical Study of Local Post-hoc XAI Methods for Fairness Exploration cites this paper.

Explanations as Bias Detectors: A Critical Study of Local Post-hoc XAI Methods for Fairness Exploration Explainability for fair machine learning

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-16T04:37:34.439799Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ed68d7f4-f9a8-4d1b-a3d6-bf69b616d820 · inbound

MESD: A Risk-Sensitive Metric for Explanation Fairness Across Intersectional Subgroups cites this paper.

MESD: A Risk-Sensitive Metric for Explanation Fairness Across Intersectional Subgroups Explainability for fair machine learning

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-05-15T11:55:33.395091Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e60ccf11-8543-4adb-8d70-8ff586a488bd · inbound

MESD: A Risk-Sensitive Metric for Explanation Fairness Across Intersectional Subgroups cites this paper.

MESD: A Risk-Sensitive Metric for Explanation Fairness Across Intersectional Subgroups Explainability for fair machine learning

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-05-21T11:30:02.493370Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1eeea081-8073-4940-b466-17a9a8026dfb · inbound

Fairness of Explanations in Artificial Intelligence (AI): A Unifying Framework, Axioms, and Future Direction toward Responsible AI cites this paper.

Fairness of Explanations in Artificial Intelligence (AI): A Unifying Framework, Axioms, and Future Direction toward Responsible AI Explainability for fair machine learning

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-12T05:41:25.277849Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7fc23563-0462-46d9-9f4e-edbd963d8c51 · inbound

Do Fair Models Reason Fairly? Counterfactual Explanation Consistency for Procedural Fairness in Credit Decisions cites this paper.

Do Fair Models Reason Fairly? Counterfactual Explanation Consistency for Procedural Fairness in Credit Decisions Explainability for fair machine learning

Reference 35

Resolution: metadata mismatch; arxiv_id, observed 2026-05-14T20:52:59.057608Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.