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Paper Citation Record · LEDGER

Data and AI governance: Promoting equity, ethics, and fairness in large language models

As of 22 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 0 inbound Pith citation observations for arXiv:2508.03970.

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

pith.paper-citation-record.v1
2508.03970 v1

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T01:03:24.414956Z

measured 38 of 38 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+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

38 of 38 outbound references displayed

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  • unresolved17
  • parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ce15dd3a-77bf-4d0a-b748-a08b790ac5e3 · outbound

This paper cites an unresolved cited work.

Data and AI governance: Promoting equity, ethics, and fairness in large language models Unresolved cited work

Reference 1

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Observation 54e26140-ba8a-460d-a33c-bc6d985b8a2c · outbound

This paper cites title Generative AI to become a \ 1.3 trillion market by 2032, research finds ( year 2023 ).

Data and AI governance: Promoting equity, ethics, and fairness in large language models title Generative AI to become a \ 1.3 trillion market by 2032, research finds ( year 2023 )

Reference 2

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Source-reported events for the cited work

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Observation 86d15621-3ae8-440a-9312-d48c5039630a · outbound

This paper cites BEATS: Bias Evaluation and Assessment Test Suite for Large Language Models.

Data and AI governance: Promoting equity, ethics, and fairness in large language models BEATS: Bias Evaluation and Assessment Test Suite for Large Language Models

Reference 3

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Observation 104d92ad-dd58-4db8-a0bb-a1c1d23dccc7 · outbound

This paper cites title Worldwide spending on artificial intelligence forecast to reach \ 632 billion in 2028 ( year 2023 ).

Data and AI governance: Promoting equity, ethics, and fairness in large language models title Worldwide spending on artificial intelligence forecast to reach \ 632 billion in 2028 ( year 2023 )

Reference 4

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Source-reported events for the cited work

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This paper cites title Generative AI market forecasts revised upward to \ 52.2b by 2028 ( year 2023 ).

Data and AI governance: Promoting equity, ethics, and fairness in large language models title Generative AI market forecasts revised upward to \ 52.2b by 2028 ( year 2023 )

Reference 5

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Source-reported events for the cited work

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This paper cites title Generative AI spending to reach \ 26 billion by 2027 ( year 2023 ).

Data and AI governance: Promoting equity, ethics, and fairness in large language models title Generative AI spending to reach \ 26 billion by 2027 ( year 2023 )

Reference 6

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Source-reported events for the cited work

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Observation c1de7721-f3a7-4e9a-94b2-6d6b588af103 · outbound

This paper cites title Generative AI market size, share, and trends 2024 to 2033 ( year 2023 ).

Data and AI governance: Promoting equity, ethics, and fairness in large language models title Generative AI market size, share, and trends 2024 to 2033 ( year 2023 )

Reference 7

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Source-reported events for the cited work

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Observation 0d42d102-478f-4f75-a2b2-601233eb30a0 · outbound

This paper cites title Spend on generative AI will grow 36\ note Online: https://www.forrester.com/blogs/spend-on-generative-ai-will-grow-36-annually-to-2030/.

Data and AI governance: Promoting equity, ethics, and fairness in large language models title Spend on generative AI will grow 36\ note Online: https://www.forrester.com/blogs/spend-on-generative-ai-will-grow-36-annually-to-2030/

Reference 8

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Source-reported events for the cited work

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Data and AI governance: Promoting equity, ethics, and fairness in large language models , author Hu, Q

Reference 9

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This paper cites & author Alhashmi, S.

Data and AI governance: Promoting equity, ethics, and fairness in large language models & author Alhashmi, S

Reference 10

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Observation 5475edd2-8c7f-4878-818a-f924bcc2e803 · outbound

This paper cites Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings.

Data and AI governance: Promoting equity, ethics, and fairness in large language models Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings

Reference 11

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Data and AI governance: Promoting equity, ethics, and fairness in large language models , author Tan, I

Reference 12

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Data and AI governance: Promoting equity, ethics, and fairness in large language models Unresolved cited work

Reference 13

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Data and AI governance: Promoting equity, ethics, and fairness in large language models , author Cai, X

Reference 14

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Data and AI governance: Promoting equity, ethics, and fairness in large language models , author Lin, E

Reference 17

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Data and AI governance: Promoting equity, ethics, and fairness in large language models Promises and pitfalls of artificial intelligence for legal applications

Reference 18

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Data and AI governance: Promoting equity, ethics, and fairness in large language models Hallucination-Free? Assessing the Reliability of Leading AI Legal Research Tools

Reference 19

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Data and AI governance: Promoting equity, ethics, and fairness in large language models , author Janssen, M

Reference 20

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Data and AI governance: Promoting equity, ethics, and fairness in large language models , author Agdas, D

Reference 21

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Reference 22

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This paper cites title The Basel Committee on Banking Supervision: A History of the Early Years, 1974--1997 ( publisher Cambridge University Press , year 2011 ).

Data and AI governance: Promoting equity, ethics, and fairness in large language models title The Basel Committee on Banking Supervision: A History of the Early Years, 1974--1997 ( publisher Cambridge University Press , year 2011 )

Reference 23

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Data and AI governance: Promoting equity, ethics, and fairness in large language models title Iso 14001 - environmental management systems — requirements with guidance for use

Reference 24

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This paper cites The AI risk repository: A meta-review, database, and taxonomy of risks from artificial intelligence.

Data and AI governance: Promoting equity, ethics, and fairness in large language models The AI risk repository: A meta-review, database, and taxonomy of risks from artificial intelligence

Reference 25

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Data and AI governance: Promoting equity, ethics, and fairness in large language models Ethical and social risks of harm from Language Models

Reference 26

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Data and AI governance: Promoting equity, ethics, and fairness in large language models , author Al-Mallah, M

Reference 27

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Data and AI governance: Promoting equity, ethics, and fairness in large language models Fairness Assessment for Artificial Intelligence in Financial Industry

Reference 29

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Reference 30

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Data and AI governance: Promoting equity, ethics, and fairness in large language models , author Cruz, L

Reference 31

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Data and AI governance: Promoting equity, ethics, and fairness in large language models title Explainable artificial intelligence ( year 2017 )

Reference 32

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Data and AI governance: Promoting equity, ethics, and fairness in large language models A Unified Approach to Interpreting Model Predictions

Reference 33

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Data and AI governance: Promoting equity, ethics, and fairness in large language models "Why Should I Trust You?": Explaining the Predictions of Any Classifier

Reference 34

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Data and AI governance: Promoting equity, ethics, and fairness in large language models Explaining Hyperparameter Optimization via Partial Dependence Plots

Reference 35

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Observation bce69013-8ffa-4beb-b043-f40396a1ce04 · outbound

This paper cites Counterfactual Explanations for Machine Learning: Challenges Revisited.

Data and AI governance: Promoting equity, ethics, and fairness in large language models Counterfactual Explanations for Machine Learning: Challenges Revisited

Reference 36

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T01:03:24.036661Z digest=sha256:41f8c57d216cecf349eb31b5ef3a9223db46019e320909d2fe95290968b558a3

Observation 758e5855-52bf-43e2-a1c6-a9fbc47d1395 · outbound

This paper cites Justice or Prejudice? Quantifying Biases in LLM-as-a-Judge.

Data and AI governance: Promoting equity, ethics, and fairness in large language models Justice or Prejudice? Quantifying Biases in LLM-as-a-Judge

Reference 37

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no resolver link, observed 2026-08-06T01:03:24.143709Z

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source=arxiv_source observed=2026-08-06T01:03:24.143709Z digest=sha256:a20c6b104846e879401b55fbf5f7288af8deb73a32990ff10cbf592d0dae2180

Observation bd230724-2782-4507-ac5c-634a103364cc · outbound

This paper cites Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena.

Data and AI governance: Promoting equity, ethics, and fairness in large language models Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-06T01:03:24.256459Z

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source=arxiv_source observed=2026-08-06T01:03:24.256459Z digest=sha256:e44c2841bf437f7fe80f76e6f45e45aad17878f5d536c8380eee1d5e3d165bec

Observation 3ac1621c-43e0-476f-beda-dcd14b85707f · outbound

This paper cites Attention Is All You Need.

Data and AI governance: Promoting equity, ethics, and fairness in large language models Attention Is All You Need

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-06T01:03:24.414956Z

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source=arxiv_source observed=2026-08-06T01:03:24.414956Z digest=sha256:e58456f5de02ec439c32da0df87dbe9cf161e580638ff487c5f76ccfaf0447f1

Pith citing papers

No inbound Pith citation observations are available.