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Paper Citation Record · LEDGER

Review of Mathematical frameworks for Fairness in Machine Learning

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2005.13755.

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

pith.paper-citation-record.v1
2005.13755 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 2026-08-12T06:34:41.77262+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:19:53.877085Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T12:49:51.935875Z

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 34edcbf3-de19-4758-8fb5-cf53bdebc001 · inbound

Fairness-Aware Grouping for Continuous Sensitive Variables: Application for Debiasing Face Analysis with respect to Skin Tone cites this paper.

Fairness-Aware Grouping for Continuous Sensitive Variables: Application for Debiasing Face Analysis with respect to Skin Tone Review of Mathematical frameworks for Fairness in Machine Learning

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-06T17:19:53.877085Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T17:19:53.877085Z digest=sha256:d57947c75c116ad2e49b09e2552b20464c3593dbf84baad3039ff3c42da8746a

Observation a4b78f5f-9ee2-4538-8caa-7773c36c8617 · inbound

FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data cites this paper.

FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data Review of Mathematical frameworks for Fairness in Machine Learning

Reference 24

Resolution
verified exact
arxiv_id, observed 2026-07-04T12:49:51.937510Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-06-26T06:07:45.509382Z digest=sha256:64dcff0cff2ba20a29f17fa4275cee92c07d5aaefb04b751809fefc36a236112