Paper Citation Record · LEDGER

Fairness in Machine Learning: A Survey

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2010.04053.

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

pith.paper-citation-record.v1
2010.04053 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:09:51.730335Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-24T12:06:11.048802Z

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 3398fae8-1ccf-41d6-9e11-7553a5f4b621 · inbound

Software Fairness: An Analysis and Survey cites this paper.

Software Fairness: An Analysis and Survey Fairness in Machine Learning: A Survey

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-05-24T12:06:11.051673Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5f22ea91-eb3f-4485-a12c-07d541d5562c · inbound

Fairness and Efficiency in Human-Agent Teams: An Iterative Algorithm Design Approach cites this paper.

Fairness and Efficiency in Human-Agent Teams: An Iterative Algorithm Design Approach Fairness in Machine Learning: A Survey

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T15:09:51.730335Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 76cd38b9-6888-4b31-9c4d-099b4666fffa · inbound

Exploring Fairness Interventions in Open Source Projects cites this paper.

Exploring Fairness Interventions in Open Source Projects Fairness in Machine Learning: A Survey

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T18:52:36.409003Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 16d12389-5940-41fa-b240-aae2d7085bb5 · inbound

Exploring the Landscape of Fairness Interventions in Software Engineering cites this paper.

Exploring the Landscape of Fairness Interventions in Software Engineering Fairness in Machine Learning: A Survey

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-06T14:35:05.610952Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 27e745af-f589-4ddc-8977-8143c021af92 · inbound

Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants cites this paper.

Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants Fairness in Machine Learning: A Survey

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-05T23:52:49.394617Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 44c7c94e-3346-4af7-bddf-995f6e6e5d85 · inbound

Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned cites this paper.

Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned Fairness in Machine Learning: A Survey

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-04T22:55:28.394835Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 505ec086-e946-4d21-a3a3-b83819459715 · inbound

Trustworthy AI: Ensuring Reliability and Accountability from Models to Agents cites this paper.

Trustworthy AI: Ensuring Reliability and Accountability from Models to Agents Fairness in Machine Learning: A Survey

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-12T02:21:16.279748Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 0123a70a-d32e-47cc-bcc7-caeb04f1a547 · inbound

Fairness vs Performance: Characterizing the Pareto Frontier of Algorithmic Decision Systems cites this paper.

Fairness vs Performance: Characterizing the Pareto Frontier of Algorithmic Decision Systems Fairness in Machine Learning: A Survey

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-12T05:41:26.836540Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5d33f375-e500-4422-8103-34da38469834 · inbound

FairDiffuseVQVAE: Sampling-Time Fairness in Tabular Diffusion via Conditional Refinement of Vector-Quantized Latents cites this paper.

FairDiffuseVQVAE: Sampling-Time Fairness in Tabular Diffusion via Conditional Refinement of Vector-Quantized Latents Fairness in Machine Learning: A Survey

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-03T16:50:34.633840Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.