Paper Citation Record · LEDGER

Generating User-friendly Explanations for Loan Denials using GANs

As of 18 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 0 inbound Pith citation observations for arXiv:1906.10244.

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

pith.paper-citation-record.v1
1906.10244 v1

Coverage vector

measured 22 of 22 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-25T17:13:41.750884Z

measured 22 of 22 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+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

22 of 22 outbound references displayed

verified exact2
verified fuzzy19
unresolved1
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6f11c5b6-1cad-48fd-b5a5-c4fb09aa5cac · outbound

This paper cites Power to the people: The role of humans in interactive machine learning.

Generating User-friendly Explanations for Loan Denials using GANs Power to the people: The role of humans in interactive machine learning

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T17:16:05.410159Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0d9d9123-0609-4cd3-b5de-cec2908f73ed · outbound

This paper cites Do explanations make vqa models more predictable to a human? EMNLP.

Generating User-friendly Explanations for Loan Denials using GANs Do explanations make vqa models more predictable to a human? EMNLP

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T17:16:05.413876Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f3074c7a-5793-44be-b70f-4e4225df90fc · outbound

This paper cites Global fintech investment robust on back of strong vc funding:kpmg.

Generating User-friendly Explanations for Loan Denials using GANs Global fintech investment robust on back of strong vc funding:kpmg

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T17:16:05.406284Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 806fa3e7-47f2-412f-aaa3-e63406368ec8 · outbound

This paper cites What does explainable ai really mean? a new conceptualization of perspectives.

Generating User-friendly Explanations for Loan Denials using GANs What does explainable ai really mean? a new conceptualization of perspectives

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T17:16:05.386182Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 901768db-029d-4472-b433-82b2a76b700c · outbound

This paper cites Towards a rigorous science of interpretable machine learning.

Generating User-friendly Explanations for Loan Denials using GANs Towards a rigorous science of interpretable machine learning

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T17:16:05.371193Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e74cbdab-ec1d-4d82-a323-8bd944dacfef · outbound

This paper cites Accountability of ai under the law: The role of explanation.

Generating User-friendly Explanations for Loan Denials using GANs Accountability of ai under the law: The role of explanation

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T17:16:05.378559Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation da50561e-83c2-45dc-a2fc-3b29fb526659 · outbound

This paper cites Machine learning and fico scores: An evolution in ml innovations that helps both lenders and consumers.

Generating User-friendly Explanations for Loan Denials using GANs Machine learning and fico scores: An evolution in ml innovations that helps both lenders and consumers

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T17:16:05.433477Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0cc8df60-dec8-49ef-8ca9-422207222632 · outbound

This paper cites xai toolkit: Practical, explainable machine learning.

Generating User-friendly Explanations for Loan Denials using GANs xai toolkit: Practical, explainable machine learning

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T17:16:05.390061Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 81148b43-f83c-4220-ad50-db7cd339c2c6 · outbound

This paper cites Explainable artificial intelligence.

Generating User-friendly Explanations for Loan Denials using GANs Explainable artificial intelligence

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T17:16:05.417743Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation bef6e9e5-642d-4092-86d5-7aae1ccf504b · outbound

This paper cites Deligan: Generative adversarial networks for diverse and limited data.

Generating User-friendly Explanations for Loan Denials using GANs Deligan: Generative adversarial networks for diverse and limited data

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T17:16:05.364116Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 21c8669d-0e69-4abf-839f-b018c75d24ae · outbound

This paper cites The promise and peril of human evaluation for model interpretability.

Generating User-friendly Explanations for Loan Denials using GANs The promise and peril of human evaluation for model interpretability

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T17:16:05.402007Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5e978c42-19cb-46b5-9f15-53c45dddec10 · outbound

This paper cites Learning overhypotheses with hierarchical bayesian models.

Generating User-friendly Explanations for Loan Denials using GANs Learning overhypotheses with hierarchical bayesian models

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T17:16:05.398309Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1768083c-ca61-4cc3-8303-1e6514d5f1ca · outbound

This paper cites CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training.

Generating User-friendly Explanations for Loan Denials using GANs CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-05-25T17:16:04.586925Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 7ba0f2de-11b7-4f19-ac45-40bee8744530 · outbound

This paper cites How explainability is driving the future of artificial intelligence.

Generating User-friendly Explanations for Loan Denials using GANs How explainability is driving the future of artificial intelligence

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T17:16:05.374987Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e956682b-4052-43c7-98e0-05304d560f40 · outbound

This paper cites The mythos of model interpretability.

Generating User-friendly Explanations for Loan Denials using GANs The mythos of model interpretability

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T17:16:05.367563Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 17c9c32d-1771-4440-856c-c4f5180e58c4 · outbound

This paper cites Explainable ai: Beware of inmates running the asylum.

Generating User-friendly Explanations for Loan Denials using GANs Explainable ai: Beware of inmates running the asylum

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T17:16:05.394161Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 023c53f0-0d34-4c93-b475-ec1bdeda7c18 · outbound

This paper cites How do humans understand explanations from machine learning systems:an evaluation of the human-interpretability of explanation.

Generating User-friendly Explanations for Loan Denials using GANs How do humans understand explanations from machine learning systems:an evaluation of the human-interpretability of explanation

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T17:16:05.425602Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 66ce9076-edd6-4629-953d-2e842b4d98e6 · outbound

This paper cites an unresolved cited work.

Generating User-friendly Explanations for Loan Denials using GANs Unresolved cited work

Reference 18

Resolution: unresolved; raw_fallback, observed 2026-05-25T17:16:05.429323Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 2b58c4b3-3c16-49fc-944b-85b06b659723 · outbound

This paper cites Explainable ai driving business value through greater understanding.

Generating User-friendly Explanations for Loan Denials using GANs Explainable ai driving business value through greater understanding

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T17:16:05.382186Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 053430e0-fe3d-4005-847b-e85ceba31a01 · outbound

This paper cites Why should i trust you? explaining the predictions of any classifier.

Generating User-friendly Explanations for Loan Denials using GANs Why should i trust you? explaining the predictions of any classifier

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T17:16:05.421592Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c83b65fe-8c2d-4e28-8481-21bce840a4d9 · outbound

This paper cites Selvaraju, A.

Generating User-friendly Explanations for Loan Denials using GANs Selvaraju, A

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T17:16:05.437099Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c645aee4-521a-40b5-bb92-efa2cca98734 · outbound

This paper cites Adversarially Regularized Autoencoders.

Generating User-friendly Explanations for Loan Denials using GANs Adversarially Regularized Autoencoders

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-05-25T17:16:04.591728Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Pith citing papers

No inbound Pith citation observations are available.