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Paper Citation Record · LEDGER

Fairness in AI Systems: Mitigating gender bias from language-vision models

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2305.01888.

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

pith.paper-citation-record.v1
2305.01888 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 2026-08-10T06:31:04.303077+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:52:56.241138Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T14:49:32.533766Z

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 69cf5b6d-47fb-40df-bdcb-0f913dfccf3e · inbound

Mitigate One, Skew Another? Tackling Intersectional Biases in Text-to-Image Models cites this paper.

Mitigate One, Skew Another? Tackling Intersectional Biases in Text-to-Image Models Fairness in AI Systems: Mitigating gender bias from language-vision models

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-07T14:52:56.241138Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:52:56.241138Z digest=sha256:4156f02e7d9d9d49028f1401bfe70c739a86b0b629d85c5aad5b57584416a0ca

Observation a43b561f-31aa-4cd9-ab0c-b9724b46fa57 · inbound

Understanding and evaluating computer vision models through the lens of counterfactuals cites this paper.

Understanding and evaluating computer vision models through the lens of counterfactuals Fairness in AI Systems: Mitigating gender bias from language-vision models

Reference 199

Resolution
verified exact
local_arxiv, observed 2026-08-05T14:49:32.553275Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-05T14:49:31.764404Z digest=sha256:8d3b9be0709de300c1fadb9069485a7e14546bb25f5a9a8ee849a2789bf395e9