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Paper Citation Record · LEDGER

humancompatible.detect: a Python Toolkit for Detecting Bias in AI Models

As of 18 August 2026, this Paper Citation Record lists 14 of 14 outbound references and 0 inbound Pith citation observations for arXiv:2509.24340.

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

pith.paper-citation-record.v1
2509.24340 v2

Coverage vector

measured 14 of 14 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T14:42:17.220465Z

measured 14 of 14 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+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

14 of 14 outbound references displayed

  • verified exact1
  • verified fuzzy0
  • unresolved13
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b35ec638-ba5a-4ae8-a83d-f85ae119bfe2 · outbound

This paper cites Julia Angwin, Jeff Larson, Lauren Kirchner, and Surya Mattu.

humancompatible.detect: a Python Toolkit for Detecting Bias in AI Models Julia Angwin, Jeff Larson, Lauren Kirchner, and Surya Mattu

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-04T14:42:17.029722Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T14:42:17.029722Z digest=sha256:1aad5fdca7357de99e2cec3c33f32ec8557b5d5b2a7500b968e714e4c80a1bdb

Observation d998ef79-3d2d-4255-88e8-c763ca0eb797 · outbound

This paper cites Sample Complexity of Bias Detection with Subsampled Point-to-Subspace Distances.

humancompatible.detect: a Python Toolkit for Detecting Bias in AI Models Sample Complexity of Bias Detection with Subsampled Point-to-Subspace Distances

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-04T14:42:17.175045Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T14:42:17.175045Z digest=sha256:b3f0871baec0191a5c3d8150c7d4a6ce543dec4e1b6298be7341316f22a489e7

Observation 059c5d22-50dd-405c-ba82-0355fca8edee · outbound

This paper cites ISBN 979-8-4007-1454-2.

humancompatible.detect: a Python Toolkit for Detecting Bias in AI Models ISBN 979-8-4007-1454-2

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-04T14:42:17.185191Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T14:42:17.185191Z digest=sha256:5a72518018bdecb790a43a1acececdfcfafc9d9824d74a93d189ebd5e2d74a98

Observation 3cc7607b-386f-4a19-a973-f312ecb7c55b · outbound

This paper cites Regulation (EU) 2024/1689.

humancompatible.detect: a Python Toolkit for Detecting Bias in AI Models Regulation (EU) 2024/1689

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-04T14:42:17.189677Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T14:42:17.189677Z digest=sha256:def21480512b0fc1c3639d94dd3e36431f72619c6b55232762292548548200c8

Observation 5bea051a-795b-4f92-851c-c93a72b0db30 · outbound

This paper cites Dimitris Sacharidis, Giorgos Giannopoulos, George Papastefanatos, and Kostas Stefanidis.

humancompatible.detect: a Python Toolkit for Detecting Bias in AI Models Dimitris Sacharidis, Giorgos Giannopoulos, George Papastefanatos, and Kostas Stefanidis

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-04T14:42:17.194089Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T14:42:17.194089Z digest=sha256:b51edd4850d2469d11520bc5eb89e597fa979d034d9b503ece87e0cfc2d3e8e0

Observation fa5873ab-5e42-4b56-bd60-50ccf8c8daf2 · outbound

This paper cites ISBN 979-8-4007-0330-0.

humancompatible.detect: a Python Toolkit for Detecting Bias in AI Models ISBN 979-8-4007-0330-0

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-04T14:42:17.210366Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T14:42:17.210366Z digest=sha256:584ccd7bb9e7140bf022cc825cf510fd714edf470769e9ffe91190c315646307

Observation ba4d2a20-3a13-443d-923f-9cfb91570413 · outbound

This paper cites 9 Matilla, Nˇemeˇcek, Kryvoviaz, and Mareˇcek Quan Zhou, Ramen Ghosh, Robert Shorten, and Jakub Mareˇ cek.

humancompatible.detect: a Python Toolkit for Detecting Bias in AI Models 9 Matilla, Nˇemeˇcek, Kryvoviaz, and Mareˇcek Quan Zhou, Ramen Ghosh, Robert Shorten, and Jakub Mareˇ cek

Reference 14

Resolution
verified exact
doi, observed 2026-08-04T14:43:32.519812Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-04T14:42:17.215611Z digest=sha256:2a80b1152c473d9448f2a7f16ee582363e6df89b95cf9309c2b69001218300b6

Observation ee2b6908-65c0-49d1-b6f6-8741de0ed538 · outbound

This paper cites URLhttp://dx.doi.org/10.1109/ ICDEW61823.2024.00029.

humancompatible.detect: a Python Toolkit for Detecting Bias in AI Models URLhttp://dx.doi.org/10.1109/ ICDEW61823.2024.00029

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-04T14:42:17.220465Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T14:42:17.220465Z digest=sha256:e06698d4da14ec38336b322b6a9f3bb9b2071c27ea50d90d5a2a8e339b50a065

Observation eb97866f-678e-4853-927e-19e9485624dd · outbound

This paper cites IEEE standard for algorithmic bias considerations.IEEE Std 7003-2024, pages 1–59,.

humancompatible.detect: a Python Toolkit for Detecting Bias in AI Models IEEE standard for algorithmic bias considerations.IEEE Std 7003-2024, pages 1–59,

Reference 2012

Resolution
unresolved
no resolver link, observed 2026-08-04T14:42:17.025373Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T14:42:17.025373Z digest=sha256:60b9977897524315a6ec4beb860c8eac54cf1a8887bcb437ae63a0384e58e208

Observation d55077be-6da7-46ea-94d2-d8bb49b2aab5 · outbound

This paper cites Andrii Kliachkin, Eleni Psaroudaki, Jakub Marecek, and Dimitris Fotakis.

humancompatible.detect: a Python Toolkit for Detecting Bias in AI Models Andrii Kliachkin, Eleni Psaroudaki, Jakub Marecek, and Dimitris Fotakis

Reference 2016

Resolution
unresolved
no resolver link, observed 2026-08-04T14:42:17.034376Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T14:42:17.034376Z digest=sha256:7bf76961d8a712e5cd56107aaa69a5a9661c4bac9b61278f6d450ce58666fb67

Observation 5ba63a7b-23dd-4765-bb5c-5fbefe7abf62 · outbound

This paper cites doi: 10.1145/3457607.

humancompatible.detect: a Python Toolkit for Detecting Bias in AI Models doi: 10.1145/3457607

Reference 2021

Resolution
unresolved
no resolver link, observed 2026-08-04T14:42:17.180582Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T14:42:17.180582Z digest=sha256:7af24f787b0a67a10ed3b98e0971045ff175a8b3db65ba873b7fd468806d21e9

Observation d1af3d87-ae4a-46d3-9079-488c77385fa6 · outbound

This paper cites Ashish Sharma, Kevin Rushton, Inna Wanyin Lin, Theresa Nguyen, and Tim Althoff.

humancompatible.detect: a Python Toolkit for Detecting Bias in AI Models Ashish Sharma, Kevin Rushton, Inna Wanyin Lin, Theresa Nguyen, and Tim Althoff

Reference 2022

Resolution
unresolved
no resolver link, observed 2026-08-04T14:42:17.205457Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T14:42:17.205457Z digest=sha256:3f0aec5a00c2641726785fb3631b3ca596a8dbaf2224a0fedbfedfc62778dfc9

Observation fba3c502-5f76-4a75-9988-ad44e7990cd0 · outbound

This paper cites Auditing for Spatial Fairness.

humancompatible.detect: a Python Toolkit for Detecting Bias in AI Models Auditing for Spatial Fairness

Reference 2023

Resolution
unresolved
no resolver link, observed 2026-08-04T14:42:17.200460Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T14:42:17.200460Z digest=sha256:26288bad620fc0e745dd4b8642ada6529153937d0abfc76c0d12bfe51cb8c5fb

Observation 497342cf-5fb4-459e-bece-efc2fdc5533c · outbound

This paper cites Arthur Gretton, Karsten M Borgwardt, Malte J Rasch, Bernhard Sch¨ olkopf, and Alexander Smola.

humancompatible.detect: a Python Toolkit for Detecting Bias in AI Models Arthur Gretton, Karsten M Borgwardt, Malte J Rasch, Bernhard Sch¨ olkopf, and Alexander Smola

Reference 2025

Resolution
unresolved
no resolver link, observed 2026-08-04T14:42:17.020136Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T14:42:17.020136Z digest=sha256:d652433263d0d62d7ff454e49b2beb9866db459bdb19cea0a70a85ed7323b03b

Pith citing papers

No inbound Pith citation observations are available.