Paper Citation Record · LEDGER

Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Them

As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:1903.03862.

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

pith.paper-citation-record.v1
1903.03862 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 11 of 11 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T14:43:33.444060Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-25T17:16:04.649808Z

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 8157b9d3-7332-4378-9c3c-4df76cd728cd · inbound

Good Secretaries, Bad Truck Drivers? Occupational Gender Stereotypes in Sentiment Analysis cites this paper.

Good Secretaries, Bad Truck Drivers? Occupational Gender Stereotypes in Sentiment Analysis Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Them

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-25T17:16:04.653645Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 293d3bac-d6d3-4e0e-b27d-a9269a9eff4a · inbound

Paired-Consistency: An Example-Based Model-Agnostic Approach to Fairness Regularization in Machine Learning cites this paper.

Paired-Consistency: An Example-Based Model-Agnostic Approach to Fairness Regularization in Machine Learning Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Them

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-14T14:43:33.444060Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation db9d83c0-7e9a-4fab-b451-a630238c432b · inbound

Debiasing Embeddings for Reduced Gender Bias in Text Classification cites this paper.

Debiasing Embeddings for Reduced Gender Bias in Text Classification Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Them

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-14T14:37:18.108376Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 740a6b55-b23a-40f8-b0d6-714c5dfda2d6 · inbound

Understanding Undesirable Word Embedding Associations cites this paper.

Understanding Undesirable Word Embedding Associations Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Them

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-14T12:54:14.096747Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b776dc25-e00f-4cbd-99e7-bd4f9699bfc7 · inbound

A Survey on Bias and Fairness in Machine Learning cites this paper.

A Survey on Bias and Fairness in Machine Learning Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Them

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-14T11:36:53.022099Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f9c06e89-42dc-4d56-a7b6-3a8fb137fa6f · inbound

Unlearn Dataset Bias in Natural Language Inference by Fitting the Residual cites this paper.

Unlearn Dataset Bias in Natural Language Inference by Fitting the Residual Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Them

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-14T10:42:14.494878Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9de08ba9-b3e7-4525-b3dd-6a101ae638f9 · inbound

Rotate King to get Queen: Word Relationships as Orthogonal Transformations in Embedding Space cites this paper.

Rotate King to get Queen: Word Relationships as Orthogonal Transformations in Embedding Space Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Them

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-14T05:55:00.596377Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9777d46d-45be-4f70-bf5c-71385a6ec3c8 · inbound

Language Models are Few-Shot Learners cites this paper.

Language Models are Few-Shot Learners Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Them

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-10T12:05:38.175809Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c1719e29-d820-4c6f-9587-712ca937234f · inbound

Mitigating Gender Bias in Contextual Word Embeddings cites this paper.

Mitigating Gender Bias in Contextual Word Embeddings Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Them

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-12T18:02:56.146297Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 22126093-5702-4f7a-a2da-1abde12cbc1f · inbound

Operationalizing Fairness in Text-to-Image Models: A Survey of Bias, Fairness Audits and Mitigation Strategies cites this paper.

Operationalizing Fairness in Text-to-Image Models: A Survey of Bias, Fairness Audits and Mitigation Strategies Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Them

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-11T11:36:02.349365Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 8ba0ec56-3f97-4bdb-b583-4138113d4ac7 · inbound

Fairness Pruning: Locating Demographic Bias in GLU-MLP Layers via Differential Activations cites this paper.

Fairness Pruning: Locating Demographic Bias in GLU-MLP Layers via Differential Activations Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Them

Reference 3

Resolution: unresolved; no resolver link, observed 2026-07-31T11:25:39.315059Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.