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Paper Citation Record · LEDGER

FairAutoML: Embracing Unfairness Mitigation in AutoML

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2111.06495.

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

pith.paper-citation-record.v1
2111.06495 v2

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 2026-08-09T06:31:02.800959+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:44:16.549731Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T09:26:01.635146Z

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 30ed7c43-4859-45ff-991e-ab485fbda89b · inbound

VirnyFlow: A Design Space for Responsible Model Development cites this paper.

VirnyFlow: A Design Space for Responsible Model Development FairAutoML: Embracing Unfairness Mitigation in AutoML

Reference 77

Resolution
unresolved
no resolver link, observed 2026-08-07T11:44:16.549731Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:44:16.549731Z digest=sha256:7b20a7e9cb64d0f369771a5b245f13833968c826bbf7e2f4905f86c4c0c06eb5

Observation d1fa3705-53e2-4e68-842c-0140628e7464 · inbound

Exploring the impact of fairness-aware criteria in AutoML cites this paper.

Exploring the impact of fairness-aware criteria in AutoML FairAutoML: Embracing Unfairness Mitigation in AutoML

Reference 17

Resolution
verified exact
arxiv_id, observed 2026-05-11T09:26:01.637991Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-10T16:01:43.023794Z digest=sha256:719ee6029ff58e2f8a3181b0ce43fbd22c908c03c30c0fdc489820ea6f750b30