Paper Citation Record · LEDGER

The Unfairness of Fair Machine Learning: Levelling down and strict egalitarianism by default

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2302.02404.

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

pith.paper-citation-record.v1
2302.02404 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T19:27:29.426274Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T07:06:44.702430Z

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 7d71edc2-9b7e-4bf8-b904-52d33206cb1a · inbound

Analyzing Fairness of Classification Machine Learning Model with Structured Dataset cites this paper.

Analyzing Fairness of Classification Machine Learning Model with Structured Dataset The Unfairness of Fair Machine Learning: Levelling down and strict egalitarianism by default

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-11T16:39:26.869046Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0d483335-54a7-48b5-b1f3-76c702b81795 · inbound

Analyzing Fairness of Computer Vision and Natural Language Processing Models cites this paper.

Analyzing Fairness of Computer Vision and Natural Language Processing Models The Unfairness of Fair Machine Learning: Levelling down and strict egalitarianism by default

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-11T16:41:06.307483Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d12d59ac-ddfe-41ec-9845-812aa6b0b024 · inbound

Transparency and Proportionality in Post-Processing Algorithmic Bias Correction cites this paper.

Transparency and Proportionality in Post-Processing Algorithmic Bias Correction The Unfairness of Fair Machine Learning: Levelling down and strict egalitarianism by default

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T14:48:03.196668Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a70a0387-2547-4c2d-803e-36d016d4d081 · inbound

External Evaluation of Discrimination Mitigation Efforts in Meta's Ad Delivery cites this paper.

External Evaluation of Discrimination Mitigation Efforts in Meta's Ad Delivery The Unfairness of Fair Machine Learning: Levelling down and strict egalitarianism by default

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-15T19:27:29.426274Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4b4ec995-46b3-41bc-9950-80a5ad7c7869 · inbound

BM-CL: Bias Mitigation through the lens of Continual Learning cites this paper.

BM-CL: Bias Mitigation through the lens of Continual Learning The Unfairness of Fair Machine Learning: Levelling down and strict egalitarianism by default

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-05T12:20:13.860989Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 56235937-4543-4ead-9366-3da34e449a9b · inbound

Adaptive Calibration for Fair and Performant Facial Recognition cites this paper.

Adaptive Calibration for Fair and Performant Facial Recognition The Unfairness of Fair Machine Learning: Levelling down and strict egalitarianism by default

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-07-02T07:06:44.704457Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.