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Paper Citation Record · LEDGER

The Pursuit of Fairness in Artificial Intelligence Models: A Survey

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2403.17333.

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

pith.paper-citation-record.v1
2403.17333 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T13:29:57.203680Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

6
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 37264f38-9276-4966-98a6-36dd2d0cf4eb · inbound

ASCenD-BDS: Adaptable, Stochastic and Context-aware framework for Detection of Bias, Discrimination and Stereotyping cites this paper.

ASCenD-BDS: Adaptable, Stochastic and Context-aware framework for Detection of Bias, Discrimination and Stereotyping The Pursuit of Fairness in Artificial Intelligence Models: A Survey

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-09T13:29:57.203680Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T13:29:57.203680Z digest=sha256:ecf39908f17a2906865f28fd74528c667f6f22a7e645bf0f3091f3a3d5a0a359

Observation 00ac2bc2-ae54-4353-9475-a4b93047ed6f · inbound

Who Defines Fairness? Target-Based Prompting for Demographic Representation in Generative Models cites this paper.

Who Defines Fairness? Target-Based Prompting for Demographic Representation in Generative Models The Pursuit of Fairness in Artificial Intelligence Models: A Survey

Reference 21

Resolution
verified exact
arxiv_id, observed 2026-05-09T23:49:44.503448Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-09T23:48:37.948835Z digest=sha256:01d85c4e7939c572f104ed8f061ac02f30a43fe735646a99098c994db3819544