Paper Citation Record · LEDGER

The Pursuit of Fairness in Artificial Intelligence Models: A Survey

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 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 8 of 8 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 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T10:21:21.228506Z

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 3a8f23ba-c036-419a-a50e-893f0629e9a1 · inbound

Programming with AI: Evaluating ChatGPT, Gemini, AlphaCode, and GitHub Copilot for Programmers cites this paper.

Programming with AI: Evaluating ChatGPT, Gemini, AlphaCode, and GitHub Copilot for Programmers The Pursuit of Fairness in Artificial Intelligence Models: A Survey

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-12T20:57:50.752676Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 434dfd69-ab49-4f3b-85cc-3ab747b38ea3 · inbound

INFELM: In-depth Fairness Evaluation of Large Text-To-Image Models cites this paper.

INFELM: In-depth Fairness Evaluation of Large Text-To-Image Models The Pursuit of Fairness in Artificial Intelligence Models: A Survey

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-10T23:48:02.324262Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7032531d-e4ae-459d-ad03-722e2c79fcea · inbound

How Do Artificial Intelligences Think? The Three Mathematico-Cognitive Factors of Categorical Segmentation Operated by Synthetic Neurons cites this paper.

How Do Artificial Intelligences Think? The Three Mathematico-Cognitive Factors of Categorical Segmentation Operated by Synthetic Neurons The Pursuit of Fairness in Artificial Intelligence Models: A Survey

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-11T00:50:28.369852Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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.

Observation 9ca9a963-88a1-4c45-90e8-8c9defeccea8 · inbound

The Process of Categorical Clipping at the Core of the Genesis of Concepts in Synthetic Neural Cognition cites this paper.

The Process of Categorical Clipping at the Core of the Genesis of Concepts in Synthetic Neural Cognition The Pursuit of Fairness in Artificial Intelligence Models: A Survey

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-10T17:37:45.811849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dffc9695-c4d3-41a1-93c8-bf1444dd50d5 · inbound

Testing Individual Fairness in Graph Neural Networks cites this paper.

Testing Individual Fairness in Graph Neural Networks The Pursuit of Fairness in Artificial Intelligence Models: A Survey

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-16T10:21:21.228506Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 86f890d2-5733-48a8-b628-8d1d768443f4 · inbound

Bias vs Bias -- Dawn of Justice: A Fair Fight in Recommendation Systems cites this paper.

Bias vs Bias -- Dawn of Justice: A Fair Fight in Recommendation Systems The Pursuit of Fairness in Artificial Intelligence Models: A Survey

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-15T18:57:51.413183Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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 2026-08-20T06:33:59.587034+00:00.