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Paper Citation Record · LEDGER

Group Fairness Meets the Black Box: Enabling Fair Algorithms on Closed LLMs via Post-Processing

As of 10 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:2508.11258.

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

pith.paper-citation-record.v1
2508.11258 v1

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T20:07:05.636256Z

measured 29 of 29 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+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

29 of 29 outbound references displayed

  • verified exact0
  • verified fuzzy13
  • unresolved15
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1aac6147-ad6a-4b02-84c3-548e06891661 · outbound

This paper cites an unresolved cited work.

Group Fairness Meets the Black Box: Enabling Fair Algorithms on Closed LLMs via Post-Processing Unresolved cited work

Reference 1

Resolution
unresolved
raw_fallback, observed 2026-08-05T20:07:07.142261Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation a5028ac7-b93e-46a3-82b4-25e1a3b506ca · outbound

This paper cites AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias.

Group Fairness Meets the Black Box: Enabling Fair Algorithms on Closed LLMs via Post-Processing AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias

Reference 3

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no resolver link, observed 2026-08-05T20:07:03.313142Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T20:07:03.313142Z digest=sha256:91f6deeb71d49aa1678c44c983da371cb9bccec6581ed80f631cae53fdb71eec

Observation 269ee9f9-6db3-425a-b1bc-2095e575d5fa · outbound

This paper cites an unresolved cited work.

Group Fairness Meets the Black Box: Enabling Fair Algorithms on Closed LLMs via Post-Processing Unresolved cited work

Reference 4

Resolution
unresolved
raw_fallback, observed 2026-08-05T20:07:09.800627Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation aa77bbb5-cb10-4790-8e2b-6689e8d1e6a0 · outbound

This paper cites Bias in Bios: A Case Study of Semantic Representation Bias in a High-Stakes Setting.

Group Fairness Meets the Black Box: Enabling Fair Algorithms on Closed LLMs via Post-Processing Bias in Bios: A Case Study of Semantic Representation Bias in a High-Stakes Setting

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T20:07:09.046498Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation 3c32c29e-501d-4919-80a3-55b97dbf0664 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Group Fairness Meets the Black Box: Enabling Fair Algorithms on Closed LLMs via Post-Processing BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 9

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation 6039e6aa-d6bd-4362-81bb-c15cdac63cb1 · outbound

This paper cites Gemma 3 Technical Report.

Group Fairness Meets the Black Box: Enabling Fair Algorithms on Closed LLMs via Post-Processing Gemma 3 Technical Report

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-05T20:07:04.015166Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T20:07:04.015166Z digest=sha256:d45951d82f931783660d8684abcb3aad72880c82e3c3fd2a66d081f55ab4eee0

Observation 877cc332-c009-4caa-b907-c8c4bb75312e · outbound

This paper cites Strategic Demonstration Selection for Improved Fairness in LLM In-Context Learning.

Group Fairness Meets the Black Box: Enabling Fair Algorithms on Closed LLMs via Post-Processing Strategic Demonstration Selection for Improved Fairness in LLM In-Context Learning

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T20:07:08.250923Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-05T20:07:04.183748Z digest=sha256:bf082c4b14ef6f8e683b98137b27ca0ce15470fe8f9f41ead6b05eef0ad3c748

Observation 9e164493-4536-4a12-b931-4ddc33871955 · outbound

This paper cites Projection to Fairness in Statistical Learning.

Group Fairness Meets the Black Box: Enabling Fair Algorithms on Closed LLMs via Post-Processing Projection to Fairness in Statistical Learning

Reference 14

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no resolver link, observed 2026-08-05T20:07:04.275082Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T20:07:04.275082Z digest=sha256:2c36c1e2662c21e0b6f394362abe73c2eb9dff2410180e83c4117b1a8986de50

Observation 7b0ab6c5-94d7-40e9-be3e-638abfcc1437 · outbound

This paper cites Fairness of ChatGPT.

Group Fairness Meets the Black Box: Enabling Fair Algorithms on Closed LLMs via Post-Processing Fairness of ChatGPT

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-05T20:07:04.362729Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T20:07:04.362729Z digest=sha256:3f6a976d314a51df5a50ca7d2c0348a9796988caf3f3ad637acf56b6e3db6f3c

Observation dec6c3cc-b415-4465-b1ac-9e6b4bfb8d4c · outbound

This paper cites The Llama 3 Herd of Models.

Group Fairness Meets the Black Box: Enabling Fair Algorithms on Closed LLMs via Post-Processing The Llama 3 Herd of Models

Reference 16

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T20:07:04.449550Z digest=sha256:dfec9553be301906fb46a567331e8a0390a02de8bfe96559b3483c3e397c5543

Observation 78d1d7e8-f4cd-49ce-94cd-d4747780d9f1 · outbound

This paper cites Williamson.

Group Fairness Meets the Black Box: Enabling Fair Algorithms on Closed LLMs via Post-Processing Williamson

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T20:07:08.036030Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-05T20:07:04.509975Z digest=sha256:2eb95f817094347456e39afa017ce57fd6ce2954ed56752d69ac5fa2277ae0e0

Observation 5b35ab21-8ea4-49a9-80e6-29daaa17fe94 · outbound

This paper cites an unresolved cited work.

Group Fairness Meets the Black Box: Enabling Fair Algorithms on Closed LLMs via Post-Processing Unresolved cited work

Reference 18

Resolution
unresolved
raw_fallback, observed 2026-08-05T20:07:07.829412Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation 151e9136-0658-4559-9aae-f1f24b03775d · outbound

This paper cites GPT-4 Technical Report.

Group Fairness Meets the Black Box: Enabling Fair Algorithms on Closed LLMs via Post-Processing GPT-4 Technical Report

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-05T20:07:04.664423Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T20:07:04.664423Z digest=sha256:115b1f6e15def8d5e26645839c06d82307a9174f0d5dcb2eab1c0f7204ee07b2

Observation 21f995d5-680b-4efe-b587-991986efb411 · outbound

This paper cites Toward a better trade-off between performance and fairness with kernel-based distribution matching.

Group Fairness Meets the Black Box: Enabling Fair Algorithms on Closed LLMs via Post-Processing Toward a better trade-off between performance and fairness with kernel-based distribution matching

Reference 21

Resolution
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no resolver link, observed 2026-08-05T20:07:04.803402Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation c67a88dc-e23c-453f-aece-ee7409a5eb2d · outbound

This paper cites A Unified Post-Processing Framework for Group Fairness in Classification.

Group Fairness Meets the Black Box: Enabling Fair Algorithms on Closed LLMs via Post-Processing A Unified Post-Processing Framework for Group Fairness in Classification

Reference 22

Resolution
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no resolver link, observed 2026-08-05T20:07:04.865930Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 651e5587-545e-49a5-bf3b-ee689447f323 · outbound

This paper cites an unresolved cited work.

Group Fairness Meets the Black Box: Enabling Fair Algorithms on Closed LLMs via Post-Processing Unresolved cited work

Reference 24

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raw_fallback, observed 2026-08-05T20:07:07.408629Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-05T20:07:05.085773Z digest=sha256:223ab49dc3e43863463e370c8da5a822978ab9491acb45a7e92f2b83c60b889d

Observation 85612daa-b87e-458f-8ab7-78c700068e59 · outbound

This paper cites {column_name}: {value}.

Group Fairness Meets the Black Box: Enabling Fair Algorithms on Closed LLMs via Post-Processing {column_name}: {value}

Reference 26

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation 96851e52-db48-4e01-a290-364ce6b34e5a · outbound

This paper cites workclass: State-gov.

Group Fairness Meets the Black Box: Enabling Fair Algorithms on Closed LLMs via Post-Processing workclass: State-gov

Reference 27

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation 7d3e7e05-6c9b-4802-a4b2-15c6fd5b1ece · outbound

This paper cites allowed fairness constraint violation.

Group Fairness Meets the Black Box: Enabling Fair Algorithms on Closed LLMs via Post-Processing allowed fairness constraint violation

Reference 28

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-05T20:07:05.511794Z digest=sha256:10bbb0bc0b8f7b9a648e352345c9e487f08526688c2b4c18594e306c104fac38

Observation 98583156-e72e-42b1-b6c4-23a0205c2967 · outbound

This paper cites Emptying.

Group Fairness Meets the Black Box: Enabling Fair Algorithms on Closed LLMs via Post-Processing Emptying

Reference 512

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T20:07:06.322166Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-05T20:07:05.636256Z digest=sha256:897bf17d8caa5b51dfbe873c8b8a66c7c7d673948cd1db91262e14a0b7fbbbac

Observation 92a6aa10-cfc7-4cf8-9965-bd57c6c9e7dd · outbound

This paper cites Bayes-Optimal Fair Classification with Linear Disparity Constraints via Pre-, In-, and Post-processing.

Group Fairness Meets the Black Box: Enabling Fair Algorithms on Closed LLMs via Post-Processing Bayes-Optimal Fair Classification with Linear Disparity Constraints via Pre-, In-, and Post-processing

Reference 2013

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T20:07:04.960335Z digest=sha256:def2a9bfb062096b25ef5a8b2b6ba28e60f90fbb2e94879d654fea62d5783be9

Observation 84dee289-b4d3-4dcc-806f-91947062449b · outbound

This paper cites James Atwood, Nino Scherrer, Preethi Lahoti, Ananth Balashankar, Flavien Prost, and Ahmad Beirami.

Group Fairness Meets the Black Box: Enabling Fair Algorithms on Closed LLMs via Post-Processing James Atwood, Nino Scherrer, Preethi Lahoti, Ananth Balashankar, Flavien Prost, and Ahmad Beirami

Reference 2016

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation d3aeb74c-842c-4b1a-9521-d6f74eb568e9 · outbound

This paper cites Nuanced Metrics for Measuring Unintended Bias with Real Data for Text Classification.

Group Fairness Meets the Black Box: Enabling Fair Algorithms on Closed LLMs via Post-Processing Nuanced Metrics for Measuring Unintended Bias with Real Data for Text Classification

Reference 2019

Resolution
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raw_fallback, observed 2026-08-05T20:07:09.542453Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-05T20:07:03.485280Z digest=sha256:45b4b98008edc0538ec22a13d8962a75a59be293a85515da65ff78d3629e54dc

Observation 98379cbd-d203-4400-87c7-abb35d254060 · outbound

This paper cites Building Classifiers with Independency Constraints.

Group Fairness Meets the Black Box: Enabling Fair Algorithms on Closed LLMs via Post-Processing Building Classifiers with Independency Constraints

Reference 2020

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T20:07:09.282069Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-05T20:07:03.576454Z digest=sha256:45475a051fd56ce088bf468634a8da1cd1bfbd49a41743d80954b2fde58e368d

Observation 34f0688a-2432-46b4-8ffe-92daf6de8430 · outbound

This paper cites Balancing out Bias: Achieving Fairness Through Balanced Training.

Group Fairness Meets the Black Box: Enabling Fair Algorithms on Closed LLMs via Post-Processing Balancing out Bias: Achieving Fairness Through Balanced Training

Reference 2021

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T20:07:08.440284Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation 4bb2e9b1-0184-4a68-95f6-090f352032a0 · outbound

This paper cites an unresolved cited work.

Group Fairness Meets the Black Box: Enabling Fair Algorithms on Closed LLMs via Post-Processing Unresolved cited work

Reference 2022

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unresolved
raw_fallback, observed 2026-08-05T20:07:07.602617Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation 18ae5e78-aeb9-49ad-bf22-9c79a526c336 · outbound

This paper cites Chi, and Alex Beutel.

Group Fairness Meets the Black Box: Enabling Fair Algorithms on Closed LLMs via Post-Processing Chi, and Alex Beutel

Reference 2023

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T20:07:08.648494Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-05T20:07:03.927899Z digest=sha256:5b027be39583779585aa06b74ef5ee33ff59b2667c4266e5b1e2f2ee4489bc20

Observation fc133ea0-76d4-4779-8ad8-ea0865b0e67d · outbound

This paper cites Improving LLM Group Fairness on Tabular Data via In-Context Learning.

Group Fairness Meets the Black Box: Enabling Fair Algorithms on Closed LLMs via Post-Processing Improving LLM Group Fairness on Tabular Data via In-Context Learning

Reference 2024

Resolution
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local_arxiv, observed 2026-08-05T20:07:05.933345Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation b2e28065-f83b-4e95-a0b5-0089aa98fe8b · outbound

This paper cites Measuring Implicit Bias in Explicitly Unbiased Large Language Models.

Group Fairness Meets the Black Box: Enabling Fair Algorithms on Closed LLMs via Post-Processing Measuring Implicit Bias in Explicitly Unbiased Large Language Models

Reference 2025

Resolution
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no resolver link, observed 2026-08-05T20:07:03.225641Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T20:07:03.225641Z digest=sha256:5b39daae4b80d48284dbbefc9cd48bb0a94244a8408cef9382a70080cdd8445d

Pith citing papers

No inbound Pith citation observations are available.