Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
The Pursuit of Fairness in Artificial Intelligence Models: A Survey
As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2403.17333.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-11T00:50:28.369852Z
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 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 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 2026-08-11T06:34:44.6726+00:00.