Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey
As of 5 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2207.07068.
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 2026-08-04T06:34:03.388597+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-07-13T05:33:08.548804Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-10T06:15:00.866473Z
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 751abb38-a207-46f2-90d1-3619bfd459f3 · inbound
FairLogue: A Toolkit for Intersectional Fairness Analysis in Clinical Machine Learning Models cites this paper.
FairLogue: A Toolkit for Intersectional Fairness Analysis in Clinical Machine Learning Models Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey
Reference 1
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T22:10:49.037864Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation b1189590-4da1-4257-8363-e3004e563360 · inbound
Evaluating Intersectional Fairness across Clinical Machine Learning Use Cases using Fairlogue and the All of Us Research Program cites this paper.
Evaluating Intersectional Fairness across Clinical Machine Learning Use Cases using Fairlogue and the All of Us Research Program Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey
Reference 3
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-11T05:30:57.114338Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 43126ad4-d53a-44ca-b3a1-fcfcb913836d · inbound
Trustworthy AI: Ensuring Reliability and Accountability from Models to Agents cites this paper.
Trustworthy AI: Ensuring Reliability and Accountability from Models to Agents Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey
Reference 74
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T02:21:16.269794Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 4bda0d66-f29c-497c-8796-a648901df0c1 · inbound
Unmasking LAION-5B: Age, Gender, Race, and Emotion Biases in Large-Scale Image Datasets cites this paper.
Unmasking LAION-5B: Age, Gender, Race, and Emotion Biases in Large-Scale Image Datasets Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey
Reference 194
- Resolution
- verified exact
- arxiv_id, observed 2026-06-26T09:19:17.061386Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 0bcb8d69-1e27-4c81-9999-d2ba2436fa01 · inbound
FairSelect: A Systematic Evaluation of Multi-Level and Intersectional Algorithmic Fairness cites this paper.
FairSelect: A Systematic Evaluation of Multi-Level and Intersectional Algorithmic Fairness Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey
Reference 1
- Resolution
- unresolved
- no resolver link, observed 2026-07-13T05:33:08.548804Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.