Paper Citation Record · LEDGER

Robust ML Auditing using Prior Knowledge

As of 22 August 2026, this Paper Citation Record lists 64 of 64 outbound references and 1 inbound Pith citation observation for arXiv:2505.04796.

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

pith.paper-citation-record.v1
2505.04796 v2

Coverage vector

measured 64 of 64 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T23:27:34.387107Z

measured 65 of 65 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-28T11:24:01.547119Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T01:56:27.963173Z

Reference resolution

64 of 64 outbound references displayed

verified exact1
verified fuzzy36
unresolved27
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d4385218-b1b6-4d21-b9b4-e6cf7effa9d1 · outbound

This paper cites write newline.

Robust ML Auditing using Prior Knowledge write newline

Reference 1

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Source-reported events for the cited work

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Observation 20f5ddf2-eb51-4d0f-a74c-c45313b542eb · outbound

This paper cites Fairwashing: the risk of rationalization.

Robust ML Auditing using Prior Knowledge Fairwashing: the risk of rationalization

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:27:35.461174Z

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Observation 08d83775-dabd-4ee8-ac79-fb8cbd25c69c · outbound

This paper cites Characterizing the risk of fairwashing.

Robust ML Auditing using Prior Knowledge Characterizing the risk of fairwashing

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:27:35.451748Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 93560dcc-10c2-4b35-a94d-9372001d0aff · outbound

This paper cites Active Fourier Auditor for Estimating Distributional Properties of ML Models , 2024.

Robust ML Auditing using Prior Knowledge Active Fourier Auditor for Estimating Distributional Properties of ML Models , 2024

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:27:35.442131Z

Source-reported events for the cited work

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Observation 2dc648c7-e542-4f39-b43d-6798bd03d4d4 · outbound

This paper cites an unresolved cited work.

Robust ML Auditing using Prior Knowledge Unresolved cited work

Reference 5

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Observation 4bc8acbb-9bc8-4224-889d-feb146e05dad · outbound

This paper cites J., Pasliev, P., Dombrowski, A., M \" u ller, K., and Kessel, P.

Robust ML Auditing using Prior Knowledge J., Pasliev, P., Dombrowski, A., M \" u ller, K., and Kessel, P

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:27:35.432312Z

Source-reported events for the cited work

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Observation 70b35d9f-cbd7-41bf-b4ba-cd8ecb9ec3c0 · outbound

This paper cites Fairness seen as global sensitivity analysis.

Robust ML Auditing using Prior Knowledge Fairness seen as global sensitivity analysis

Reference 7

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Source-reported events for the cited work

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Observation 252babc9-49cd-43e7-b836-f88a6a2a2375 · outbound

This paper cites Provable Fairness for Neural Network Models using Formal Verification , 2022.

Robust ML Auditing using Prior Knowledge Provable Fairness for Neural Network Models using Formal Verification , 2022

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:27:35.423174Z

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Observation 8851d105-75c5-43be-9521-49dc077434b2 · outbound

This paper cites and Bie, T.

Robust ML Auditing using Prior Knowledge and Bie, T

Reference 10

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Observation c1ca0a9b-1f56-4812-a179-a085ec37cd80 · outbound

This paper cites Building Classifiers with Independency Constraints.

Robust ML Auditing using Prior Knowledge Building Classifiers with Independency Constraints

Reference 11

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Source-reported events for the cited work

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Observation f9b05209-876f-4559-9e61-a43f952fb2f2 · outbound

This paper cites and Haas, C.

Robust ML Auditing using Prior Knowledge and Haas, C

Reference 12

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Source-reported events for the cited work

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Observation 0b799f97-36fe-42d0-9fd3-f77bd5756ed4 · outbound

This paper cites an unresolved cited work.

Robust ML Auditing using Prior Knowledge Unresolved cited work

Reference 13

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Source-reported events for the cited work

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Observation 04b5fb3b-3289-447d-ab02-d9aafc8db82f · outbound

This paper cites an unresolved cited work.

Robust ML Auditing using Prior Knowledge Unresolved cited work

Reference 14

Resolution: unresolved; raw_fallback, observed 2026-08-15T23:27:35.394780Z

Source-reported events for the cited work

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Observation 8de844f6-a706-4af0-ac12-ebe0505c997c · outbound

This paper cites Auditing fairness by betting.

Robust ML Auditing using Prior Knowledge Auditing fairness by betting

Reference 15

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Source-reported events for the cited work

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Observation 3a57cb7b-bb74-4801-b5d2-7bf709315b13 · outbound

This paper cites Algorithmic accountability act of 2022.

Robust ML Auditing using Prior Knowledge Algorithmic accountability act of 2022

Reference 16

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Observation 306af347-e8e8-43c0-8a98-6f1239b12863 · outbound

This paper cites D., and Buolamwini, J.

Robust ML Auditing using Prior Knowledge D., and Buolamwini, J

Reference 17

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 42f3b158-05f1-44ae-b46c-cae03e0b0113 · outbound

This paper cites Enforcing the digital markets act: institutional choices, compliance, and antitrust.

Robust ML Auditing using Prior Knowledge Enforcing the digital markets act: institutional choices, compliance, and antitrust

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:27:35.359981Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 01d5b330-c6aa-486a-b979-e21a4be0c248 · outbound

This paper cites Fairness auditing with multi-agent collaboration.

Robust ML Auditing using Prior Knowledge Fairness auditing with multi-agent collaboration

Reference 19

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 46756ce1-2c61-4b16-a716-36a83d5d8ac7 · outbound

This paper cites a \" a n \.

Robust ML Auditing using Prior Knowledge a \" a n \

Reference 20

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Source-reported events for the cited work

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Observation dfb2ce44-dcb8-4006-b976-1bb5c1857142 · outbound

This paper cites Evaluating fairness using permutation tests.

Robust ML Auditing using Prior Knowledge Evaluating fairness using permutation tests

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:27:35.340471Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 049ffc4c-5c08-4e44-a7cc-f68ab3bab609 · outbound

This paper cites Retiring adult: New datasets for fair machine learning.

Robust ML Auditing using Prior Knowledge Retiring adult: New datasets for fair machine learning

Reference 22

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 130e074c-43ee-40f0-aadc-d307a8604d29 · outbound

This paper cites Attribution-based Explanations that Provide Recourse Cannot be Robust.

Robust ML Auditing using Prior Knowledge Attribution-based Explanations that Provide Recourse Cannot be Robust

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:27:35.320881Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 0694bb34-5d51-412e-8f90-6cbf89c9add6 · outbound

This paper cites Faking fairness via stealthily biased sampling.

Robust ML Auditing using Prior Knowledge Faking fairness via stealthily biased sampling

Reference 24

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation dbeef05b-3899-48dc-8a1e-720fa8bc3528 · outbound

This paper cites On the relevance of APIs facing fairwashed audits, 2023.

Robust ML Auditing using Prior Knowledge On the relevance of APIs facing fairwashed audits, 2023

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:27:35.301167Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation db11d58c-ea66-41a5-9516-60d3d732fbab · outbound

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Robust ML Auditing using Prior Knowledge Unresolved cited work

Reference 26

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Source-reported events for the cited work

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Observation 3bcc171c-6bf9-4059-87bf-ee951b8b55e0 · outbound

This paper cites an unresolved cited work.

Robust ML Auditing using Prior Knowledge Unresolved cited work

Reference 27

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 311910be-86be-4b98-988a-c616ac92ef83 · outbound

This paper cites Under manipulations, are some AI models harder to audit? In 2024 IEEE Conference on Secure and Trustworthy Machine Learning ( SaTML ) , pp.\ 644--664, 2024.

Robust ML Auditing using Prior Knowledge Under manipulations, are some AI models harder to audit? In 2024 IEEE Conference on Secure and Trustworthy Machine Learning ( SaTML ) , pp.\ 644--664, 2024

Reference 28

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Source-reported events for the cited work

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Observation 6e6d3514-d764-4cf0-ba19-1fb9f01c2c64 · outbound

This paper cites Equality of opportunity in supervised learning.

Robust ML Auditing using Prior Knowledge Equality of opportunity in supervised learning

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:27:35.271053Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 10e0dd3f-7759-4039-82ff-e6f4a95f5be5 · outbound

This paper cites Wasserstein fair classification.

Robust ML Auditing using Prior Knowledge Wasserstein fair classification

Reference 30

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 88baffdb-0fb0-48c1-a11e-e850578ea909 · outbound

This paper cites Decision theory for discrimination-aware classification.

Robust ML Auditing using Prior Knowledge Decision theory for discrimination-aware classification

Reference 31

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Source-reported events for the cited work

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Observation 7a3bf5df-7888-49b7-8b5e-b284abf2b017 · outbound

This paper cites P., Ghorbani, A., and Zou, J.

Robust ML Auditing using Prior Knowledge P., Ghorbani, A., and Zou, J

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:27:35.244618Z

Source-reported events for the cited work

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Observation 31643674-68b8-4b5d-8e88-301f6431adf4 · outbound

This paper cites Fooling SHAP with stealthily biased sampling.

Robust ML Auditing using Prior Knowledge Fooling SHAP with stealthily biased sampling

Reference 33

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Observation ba9b06d1-0b92-4130-86e7-48e038f8f524 · outbound

This paper cites S., Pandit, A., Kalicki, C.

Robust ML Auditing using Prior Knowledge S., Pandit, A., Kalicki, C

Reference 34

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This paper cites and Tr \'e dan, G.

Robust ML Auditing using Prior Knowledge and Tr \'e dan, G

Reference 35

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Observation 8f3a666d-61a5-488c-88d4-0ee4045c399e · outbound

This paper cites Gradient-based learning applied to document recognition.

Robust ML Auditing using Prior Knowledge Gradient-based learning applied to document recognition

Reference 36

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Source-reported events for the cited work

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Observation 52225d77-0284-4d4c-b818-d379fb975f72 · outbound

This paper cites Concise formulas for the area and volume of a hyperspherical cap.

Robust ML Auditing using Prior Knowledge Concise formulas for the area and volume of a hyperspherical cap

Reference 37

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Source-reported events for the cited work

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Observation 0663c40b-364a-441a-84f1-7e03ac705137 · outbound

This paper cites Deep learning face attributes in the wild.

Robust ML Auditing using Prior Knowledge Deep learning face attributes in the wild

Reference 38

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Source-reported events for the cited work

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Observation 65aac902-41a1-4910-baa0-50bed74e0557 · outbound

This paper cites Too relaxed to be fair.

Robust ML Auditing using Prior Knowledge Too relaxed to be fair

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:27:35.218373Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 9e923040-44cc-4238-8032-518070c148a4 · outbound

This paper cites Online fairness auditing through iterative refinement.

Robust ML Auditing using Prior Knowledge Online fairness auditing through iterative refinement

Reference 40

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Source-reported events for the cited work

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Observation f2ab61bb-a3b7-4316-8f0d-ffe653e74a84 · outbound

This paper cites A survey on bias and fairness in machine learning.

Robust ML Auditing using Prior Knowledge A survey on bias and fairness in machine learning

Reference 41

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Observation 95535d54-e080-4031-9560-3ef3141c8e5f · outbound

This paper cites Reasons to Doubt the Impact of AI Risk Evaluations.

Robust ML Auditing using Prior Knowledge Reasons to Doubt the Impact of AI Risk Evaluations

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-15T23:27:34.318101Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0f6e3c24-4687-4330-b205-f86e0040bc7f · outbound

This paper cites Can auditing eliminate bias from algorithms?, 2021.

Robust ML Auditing using Prior Knowledge Can auditing eliminate bias from algorithms?, 2021

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:27:35.201046Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 0ea538c0-21fe-4fff-959a-c0cc80a33228 · outbound

This paper cites Nist digital library of mathematical functions, 2013.

Robust ML Auditing using Prior Knowledge Nist digital library of mathematical functions, 2013

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:27:35.190521Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation ef3b511c-98e6-48bb-becc-76c963a39021 · outbound

This paper cites Responsible and regulatory conform machine learning for medicine: a survey of challenges and solutions.

Robust ML Auditing using Prior Knowledge Responsible and regulatory conform machine learning for medicine: a survey of challenges and solutions

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:27:35.180562Z

Source-reported events for the cited work

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This paper cites M., Hanna, A., and Paullada, A.

Robust ML Auditing using Prior Knowledge M., Hanna, A., and Paullada, A

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:27:35.170314Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation acc3fbda-598d-4062-aa0e-8cab845d4716 · outbound

This paper cites an unresolved cited work.

Robust ML Auditing using Prior Knowledge Unresolved cited work

Reference 47

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This paper cites D., Xu, P., Honigsberg, C., and Ho, D.

Robust ML Auditing using Prior Knowledge D., Xu, P., Honigsberg, C., and Ho, D

Reference 48

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Source-reported events for the cited work

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Observation c6c4f33a-acfb-4e14-814b-b59476a990d3 · outbound

This paper cites an unresolved cited work.

Robust ML Auditing using Prior Knowledge Unresolved cited work

Reference 49

Resolution: unresolved; raw_fallback, observed 2026-08-15T23:27:35.160117Z

Source-reported events for the cited work

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Observation 0f65049a-6d86-4f22-8537-4fc8e8812d8d · outbound

This paper cites Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead.

Robust ML Auditing using Prior Knowledge Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-15T23:27:34.344263Z

Source-reported events for the cited work

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Observation c9ad2a74-d248-48fb-bf9d-63a082bbf546 · outbound

This paper cites S., Yaghini, M., Dullerud, N., Wyllie, S.

Robust ML Auditing using Prior Knowledge S., Yaghini, M., Dullerud, N., Wyllie, S

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:27:35.142886Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 81e6dae1-01ff-4e9f-afaa-7dcbd1edcd21 · outbound

This paper cites S., Wyllie, S.

Robust ML Auditing using Prior Knowledge S., Wyllie, S

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:27:35.131739Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 71237110-e6fd-45f0-b249-cad7ea875fb9 · outbound

This paper cites H., and Nguyen, V.

Robust ML Auditing using Prior Knowledge H., and Nguyen, V

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:27:35.121655Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 444489a5-2456-4ab2-814d-c3cb764e6deb · outbound

This paper cites Fooling LIME and SHAP : Adversarial Attacks on Post hoc Explanation Methods.

Robust ML Auditing using Prior Knowledge Fooling LIME and SHAP : Adversarial Attacks on Post hoc Explanation Methods

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-15T23:27:34.354997Z

Source-reported events for the cited work

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Observation 533ca6d9-5570-47fd-9386-58a53d0179d4 · outbound

This paper cites Distill-and- Compare : Auditing Black-Box Models Using Transparent Model Distillation.

Robust ML Auditing using Prior Knowledge Distill-and- Compare : Auditing Black-Box Models Using Transparent Model Distillation

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-15T23:27:34.357616Z

Source-reported events for the cited work

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Observation 0e386483-58cd-493e-936c-6b84771c8196 · outbound

This paper cites an unresolved cited work.

Robust ML Auditing using Prior Knowledge Unresolved cited work

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-15T23:27:34.360180Z

Source-reported events for the cited work

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Observation 4ca119b7-c852-4423-978c-5048ca62ef88 · outbound

This paper cites Facebook made big mistake in data it provided to researchers, undermining academic work.

Robust ML Auditing using Prior Knowledge Facebook made big mistake in data it provided to researchers, undermining academic work

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:27:35.111152Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 7cdb9d1d-95a7-4e2a-b56f-6ff5eab69a49 · outbound

This paper cites Regulation (eu) 2022/1925 of the european parliament and of the council of 14 september 2022 on contestable and fair markets in the digital sector (digital markets act).

Robust ML Auditing using Prior Knowledge Regulation (eu) 2022/1925 of the european parliament and of the council of 14 september 2022 on contestable and fair markets in the digital sector (digital markets act)

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:27:35.101508Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 4e1157c0-d34f-4e41-99ac-2ed8da05289c · outbound

This paper cites Trustless audits without revealing data or models.

Robust ML Auditing using Prior Knowledge Trustless audits without revealing data or models

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:27:35.092273Z

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Observation bcc33bd1-16a4-4aa3-a685-ded629fef1b0 · outbound

This paper cites Neural network credit scoring models.

Robust ML Auditing using Prior Knowledge Neural network credit scoring models

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-15T23:27:34.370733Z

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Observation 29775df5-d7ed-4413-b4c6-c9832fbb141d · outbound

This paper cites XAudit : A Theoretical Look at Auditing with Explanations , 2022.

Robust ML Auditing using Prior Knowledge XAudit : A Theoretical Look at Auditing with Explanations , 2022

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:27:35.075983Z

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Observation 719b2501-f7eb-4818-a2d0-30bfff5193a4 · outbound

This paper cites R., Boneh, D., and Chaudhuri, K.

Robust ML Auditing using Prior Knowledge R., Boneh, D., and Chaudhuri, K

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:27:35.064869Z

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Observation 8c0e3521-acc5-41a6-8d8c-406c9fc5f45e · outbound

This paper cites and Zhang, C.

Robust ML Auditing using Prior Knowledge and Zhang, C

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:27:35.054459Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 4bfa3013-ae7f-41fa-ab39-d778e260b59b · outbound

This paper cites and Gordon, G.

Robust ML Auditing using Prior Knowledge and Gordon, G

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:27:35.044007Z

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Observation 41848e54-8552-4ccf-991f-d87a259822f3 · outbound

This paper cites Leveling down in computer vision: Pareto inefficiencies in fair deep classifiers.

Robust ML Auditing using Prior Knowledge Leveling down in computer vision: Pareto inefficiencies in fair deep classifiers

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-15T23:27:34.387107Z

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Pith citing papers

Observation f1875230-0676-4c8e-8653-641e6ca5d79d · inbound

FLIPS: Instance-Fingerprinting for LLMs via Pseudo-random Sequences cites this paper.

FLIPS: Instance-Fingerprinting for LLMs via Pseudo-random Sequences Robust ML Auditing using Prior Knowledge

Reference 44

Resolution: verified exact; arxiv_id, observed 2026-07-02T01:56:27.964927Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.