Paper Citation Record · LEDGER

Fairness Audits of Institutional Risk Models in Deployed ML Pipelines

As of 14 August 2026, this Paper Citation Record lists 13 of 13 outbound references and 0 inbound Pith citation observations for arXiv:2604.19468.

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

pith.paper-citation-record.v1
2604.19468 v1

Coverage vector

measured 13 of 13 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-10T01:35:33.964568Z

measured 13 of 13 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

13 of 13 outbound references displayed

verified exact4
verified fuzzy2
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch7

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a50c0414-1ad3-4ed9-a3c4-895e178045bb · outbound

This paper cites Risk, Retention, and the Algorithmic Institu- tion: Artificial Intelligence as a Policy Response to Higher Education in Crisis.

Fairness Audits of Institutional Risk Models in Deployed ML Pipelines Risk, Retention, and the Algorithmic Institu- tion: Artificial Intelligence as a Policy Response to Higher Education in Crisis

Reference 1

Resolution: verified exact; doi, observed 2026-05-10T01:35:54.574071Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 874e4246-f81f-4b7e-9431-b941852fbd68 · outbound

This paper cites “This Is Not a Data Problem.

Fairness Audits of Institutional Risk Models in Deployed ML Pipelines “This Is Not a Data Problem

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T09:17:48.326831Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation f6f162fb-f8d4-4822-a7ff-e0b751417936 · outbound

This paper cites Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency , location =.

Fairness Audits of Institutional Risk Models in Deployed ML Pipelines Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency , location =

Reference 3

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T01:35:54.581430Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation e1a82870-36cd-495d-964e-371de35db6ea · outbound

This paper cites A Framework of High-Stakes Algorithmic Decision-Making for the Public Sector Developed through a Case Study of Child- Welfare.

Fairness Audits of Institutional Risk Models in Deployed ML Pipelines A Framework of High-Stakes Algorithmic Decision-Making for the Public Sector Developed through a Case Study of Child- Welfare

Reference 4

Resolution: verified exact; doi, observed 2026-05-10T01:35:54.572385Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 514951ed-8b44-4ac5-a015-30362c1e5649 · outbound

This paper cites InProceedings of the CHI Conference on Human Factors in Computing Systems.

Fairness Audits of Institutional Risk Models in Deployed ML Pipelines InProceedings of the CHI Conference on Human Factors in Computing Systems

Reference 5

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T01:35:54.576684Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation b2b5c99b-a06d-44bb-8684-ac2f692b9b23 · outbound

This paper cites They Shall Be Fair, Transparent, and Robust: Auditing Learning Analytics Systems.

Fairness Audits of Institutional Risk Models in Deployed ML Pipelines They Shall Be Fair, Transparent, and Robust: Auditing Learning Analytics Systems

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T09:17:48.330868Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 39bf9177-1815-4b25-a8f4-68dce50e8bbf · outbound

This paper cites Science , author =.

Fairness Audits of Institutional Risk Models in Deployed ML Pipelines Science , author =

Reference 7

Resolution: metadata mismatch; doi, observed 2026-05-10T01:35:54.578439Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 47b5e00c-9b86-4262-8c7e-28ce14efe777 · outbound

This paper cites Proceedings of the Conference on Fairness, Accountability, and Transparency , pages =.

Fairness Audits of Institutional Risk Models in Deployed ML Pipelines Proceedings of the Conference on Fairness, Accountability, and Transparency , pages =

Reference 8

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T01:35:54.570474Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation b79b9a23-65bd-4242-83e3-c368d07aa88d · outbound

This paper cites Jacobs and Hanna Wallach.

Fairness Audits of Institutional Risk Models in Deployed ML Pipelines Jacobs and Hanna Wallach

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-10T01:35:54.589607Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation dfa7d732-df34-4579-bb0f-a08667c41adc · outbound

This paper cites Difficult Lessons on Social Prediction from Wisconsin Public Schools.

Fairness Audits of Institutional Risk Models in Deployed ML Pipelines Difficult Lessons on Social Prediction from Wisconsin Public Schools

Reference 10

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T13:31:03.298936Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation ed638369-eb94-42a1-a10c-955af8a0b0df · outbound

This paper cites A Human-Centered Review of Algorithms in Decision-MakinginHigherEducation.

Fairness Audits of Institutional Risk Models in Deployed ML Pipelines A Human-Centered Review of Algorithms in Decision-MakinginHigherEducation

Reference 11

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T01:35:54.584530Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation aacdd2cb-1843-43f3-9318-1d10f83da321 · outbound

This paper cites Balancing Fairness: Unveiling the Potential of SMOTE-Driven Oversampling in AI Model Enhancement.

Fairness Audits of Institutional Risk Models in Deployed ML Pipelines Balancing Fairness: Unveiling the Potential of SMOTE-Driven Oversampling in AI Model Enhancement

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-10T01:35:54.587230Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 0ef9423d-faf9-48e5-aee1-d2f112ba3e3b · outbound

This paper cites In: Proceedings of the 3rd Innovations in Theoretica l Computer Science Conference On - ITCS ’12, pp.

Fairness Audits of Institutional Risk Models in Deployed ML Pipelines In: Proceedings of the 3rd Innovations in Theoretica l Computer Science Conference On - ITCS ’12, pp

Reference 13

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T01:35:54.592385Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Pith citing papers

No inbound Pith citation observations are available.