Paper Citation Record · LEDGER

Evaluating Explainability: A Framework for Systematic Assessment and Reporting of Explainable AI Features

As of 16 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 1 inbound Pith citation observation for arXiv:2506.13917.

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

pith.paper-citation-record.v1
2506.13917 v1

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T00:30:22.373700Z

measured 29 of 29 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-10T18:21:16.402501Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T00:45:49.509911Z

Reference resolution

28 of 28 outbound references displayed

verified exact1
verified fuzzy14
unresolved9
parse uncertain0
malformed identifier2
metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 46e71d09-bcf0-40f3-a78a-583f3e935b36 · outbound

This paper cites Sanity checks for saliency maps.Advances in neural information processing systems, 31, 2018.

Evaluating Explainability: A Framework for Systematic Assessment and Reporting of Explainable AI Features Sanity checks for saliency maps.Advances in neural information processing systems, 31, 2018

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:30:27.168247Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation bb5b4430-7ee2-439d-afe6-de31255d1e80 · outbound

This paper cites Artificial intelligence risk management framework (ai rmf 1.0).

Evaluating Explainability: A Framework for Systematic Assessment and Reporting of Explainable AI Features Artificial intelligence risk management framework (ai rmf 1.0)

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:30:27.012467Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation f08b84b5-4842-438e-a9a6-c5a143e0654b · outbound

This paper cites an unresolved cited work.

Evaluating Explainability: A Framework for Systematic Assessment and Reporting of Explainable AI Features Unresolved cited work

Reference 3

Resolution: unresolved; raw_fallback, observed 2026-08-07T00:30:26.782821Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b640a59e-7a6d-400e-bc54-e122d26264f3 · outbound

This paper cites Psychological foundations of explainability and interpretability in artificial intelligence.NIST, Tech.

Evaluating Explainability: A Framework for Systematic Assessment and Reporting of Explainable AI Features Psychological foundations of explainability and interpretability in artificial intelligence.NIST, Tech

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:30:26.569699Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b4b3546e-7d52-4e6c-b21f-91df17c65a1c · outbound

This paper cites IOS Press, 2020.

Evaluating Explainability: A Framework for Systematic Assessment and Reporting of Explainable AI Features IOS Press, 2020

Reference 5

Resolution: verified exact; doi, observed 2026-08-07T00:30:22.596259Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d5d12cf9-dbd7-4e84-9cc1-c0376e5c629a · outbound

This paper cites Moore, Marinka Zitnik, and John H.

Evaluating Explainability: A Framework for Systematic Assessment and Reporting of Explainable AI Features Moore, Marinka Zitnik, and John H

Reference 6

Resolution: metadata mismatch; raw_fallback, observed 2026-08-07T00:30:23.527241Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 311dd1ae-6297-47df-9b24-1a4db4bbbc7a · outbound

This paper cites Explainable arti- ficial intelligence (xai) in radiology and nuclear medicine: a literature review.Frontiers in medicine, 10:1180773, 2023.

Evaluating Explainability: A Framework for Systematic Assessment and Reporting of Explainable AI Features Explainable arti- ficial intelligence (xai) in radiology and nuclear medicine: a literature review.Frontiers in medicine, 10:1180773, 2023

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:30:26.359496Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 49119f2f-6340-42d3-8664-d9ed4f09d542 · outbound

This paper cites Ramaswamy.

Evaluating Explainability: A Framework for Systematic Assessment and Reporting of Explainable AI Features Ramaswamy

Reference 8

Resolution: malformed identifier; no resolver link, observed 2026-08-07T00:30:19.975339Z

Source-reported events for the cited work

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Observation 340aa15e-f030-45c0-92ba-b1444acde00e · outbound

This paper cites Towards A Rigorous Science of Interpretable Machine Learning.

Evaluating Explainability: A Framework for Systematic Assessment and Reporting of Explainable AI Features Towards A Rigorous Science of Interpretable Machine Learning

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T00:30:20.145602Z

Source-reported events for the cited work

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Observation 8322acdf-414d-4e74-ac37-f9182f649ed4 · outbound

This paper cites From heatmaps to structured explanations of image classifiers.Applied AI Letters, 2(4):e46, 2021.

Evaluating Explainability: A Framework for Systematic Assessment and Reporting of Explainable AI Features From heatmaps to structured explanations of image classifiers.Applied AI Letters, 2(4):e46, 2021

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:30:26.080492Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation bfdf74d5-2278-4c9e-bc0a-ce92fc19bff6 · outbound

This paper cites Gilpin, David Bau, Ben Z.

Evaluating Explainability: A Framework for Systematic Assessment and Reporting of Explainable AI Features Gilpin, David Bau, Ben Z

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T00:30:20.530058Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7273ff8f-1073-45ad-a577-63ea89634c14 · outbound

This paper cites A survey of methods for explaining black box models.ACM computing surveys (CSUR), 51(5):1–42, 2018.

Evaluating Explainability: A Framework for Systematic Assessment and Reporting of Explainable AI Features A survey of methods for explaining black box models.ACM computing surveys (CSUR), 51(5):1–42, 2018

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T00:30:20.716848Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f444ab24-423e-468f-938b-0a6a792b2cb5 · outbound

This paper cites Darpa’s explainable ai (xai) program: A retrospective.Applied AI Letters, 2(4):e61, 2021.

Evaluating Explainability: A Framework for Systematic Assessment and Reporting of Explainable AI Features Darpa’s explainable ai (xai) program: A retrospective.Applied AI Letters, 2(4):e61, 2021

Reference 13

Resolution: malformed identifier; raw_fallback, observed 2026-08-07T00:30:25.899145Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation dbf6c3d4-eefc-445f-aea5-b79f47128613 · outbound

This paper cites An analysis of explainability methods for convolutional neural networks.Engineering Applications of Artificial Intelligence, 2023.

Evaluating Explainability: A Framework for Systematic Assessment and Reporting of Explainable AI Features An analysis of explainability methods for convolutional neural networks.Engineering Applications of Artificial Intelligence, 2023

Reference 14

Resolution: metadata mismatch; raw_fallback, observed 2026-08-07T00:30:23.059097Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 1dade9ab-fdbc-47fb-ab64-be7679cfd7a4 · outbound

This paper cites Extract interpretability-accuracy balanced rules from artificial neural networks: A review.Neurocomputing, 387:346–358, 2020.

Evaluating Explainability: A Framework for Systematic Assessment and Reporting of Explainable AI Features Extract interpretability-accuracy balanced rules from artificial neural networks: A review.Neurocomputing, 387:346–358, 2020

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:30:25.729454Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 7a3f668e-af88-4088-aa2b-9dc79476151b · outbound

This paper cites Mouton, Md Sirajus Salekin, Yu Sun, and Dmitry Goldgof.

Evaluating Explainability: A Framework for Systematic Assessment and Reporting of Explainable AI Features Mouton, Md Sirajus Salekin, Yu Sun, and Dmitry Goldgof

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T00:30:21.336526Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 11fea970-6585-4749-9f88-66f70438736d · outbound

This paper cites Measuring the impact of ai in the diagnosis of hospitalized patients: a randomized clinical vignette survey study.JAMA, 330 (23):2275–2284, 2023.

Evaluating Explainability: A Framework for Systematic Assessment and Reporting of Explainable AI Features Measuring the impact of ai in the diagnosis of hospitalized patients: a randomized clinical vignette survey study.JAMA, 330 (23):2275–2284, 2023

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:30:25.511690Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 5b33d7ab-89bf-4af0-b2c0-a9cb6166ffe3 · outbound

This paper cites Automation bias and assistive ai: Risk of harm from ai-driven clinical decision support.JAMA, 330(23):2255–2257, 2023.

Evaluating Explainability: A Framework for Systematic Assessment and Reporting of Explainable AI Features Automation bias and assistive ai: Risk of harm from ai-driven clinical decision support.JAMA, 330(23):2255–2257, 2023

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:30:25.252296Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 0d9bbc15-e45a-4459-807c-0bc990d6dc52 · outbound

This paper cites Interpretable decision sets: A joint framework for description and prediction.

Evaluating Explainability: A Framework for Systematic Assessment and Reporting of Explainable AI Features Interpretable decision sets: A joint framework for description and prediction

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:30:25.041336Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 47b2f29a-346e-45b5-a845-f875e7610b2a · outbound

This paper cites The acr learn- ing network: facilitating local performance improvement through shared learning.Journal of the American College of Radiology, 20(3):369–376, 2023.

Evaluating Explainability: A Framework for Systematic Assessment and Reporting of Explainable AI Features The acr learn- ing network: facilitating local performance improvement through shared learning.Journal of the American College of Radiology, 20(3):369–376, 2023

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:30:24.813688Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 0fed6ef4-4860-4ddc-be49-6971e1ce6667 · outbound

This paper cites Eigen-cam: Class activation map using principal components.

Evaluating Explainability: A Framework for Systematic Assessment and Reporting of Explainable AI Features Eigen-cam: Class activation map using principal components

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T00:30:21.783940Z

Source-reported events for the cited work

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Observation 81221d96-b5c2-4a2a-82b1-bc9d7b837a2f · outbound

This paper cites From anecdotal evidence to quantitative evaluation methods: A systematic review on evaluating explainable ai.ACM Computing Surveys, 55(13s):1–42, 2023.

Evaluating Explainability: A Framework for Systematic Assessment and Reporting of Explainable AI Features From anecdotal evidence to quantitative evaluation methods: A systematic review on evaluating explainable ai.ACM Computing Surveys, 55(13s):1–42, 2023

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:30:24.598061Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 2bbcf028-3781-431b-b6b5-5105bcbae93f · outbound

This paper cites Faster r-cnn: Towards real-time object detection with region proposal networks.IEEE transactions on pattern analysis and machine intelligence, 39(6):1137–1149, 2016.

Evaluating Explainability: A Framework for Systematic Assessment and Reporting of Explainable AI Features Faster r-cnn: Towards real-time object detection with region proposal networks.IEEE transactions on pattern analysis and machine intelligence, 39(6):1137–1149, 2016

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T00:30:21.974651Z

Source-reported events for the cited work

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Observation a8d00137-6d31-4146-8d93-7d874dc3ff0d · outbound

This paper cites Anchors: high-precision model- agnostic explanations.

Evaluating Explainability: A Framework for Systematic Assessment and Reporting of Explainable AI Features Anchors: high-precision model- agnostic explanations

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:30:24.385989Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 9d5f9603-ef84-4f17-bdca-a5c0a2d103f4 · outbound

This paper cites Quantifying Interpretability and Trust in Machine Learning Systems.

Evaluating Explainability: A Framework for Systematic Assessment and Reporting of Explainable AI Features Quantifying Interpretability and Trust in Machine Learning Systems

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T00:30:22.114835Z

Source-reported events for the cited work

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Observation 3adc5d5f-8498-4b7e-96c6-6fc79c8faf30 · outbound

This paper cites an unresolved cited work.

Evaluating Explainability: A Framework for Systematic Assessment and Reporting of Explainable AI Features Unresolved cited work

Reference 26

Resolution: unresolved; raw_fallback, observed 2026-08-07T00:30:24.210840Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 0c5e44d3-813e-44a2-bf93-48d621b460aa · outbound

This paper cites Gradient-based saliency maps are not trustworthy visual explanations of automated ai mus- culoskeletal diagnoses.Journal of Imaging Informatics in Medicine, pages 1–10, 2024.

Evaluating Explainability: A Framework for Systematic Assessment and Reporting of Explainable AI Features Gradient-based saliency maps are not trustworthy visual explanations of automated ai mus- culoskeletal diagnoses.Journal of Imaging Informatics in Medicine, pages 1–10, 2024

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:30:23.996872Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 783d9ea3-7132-4a5d-80e2-e177e95b3236 · outbound

This paper cites Notions of explainability and evaluation approaches for explainable artificial intelligence.Information Fusion, 76:89–106, 2021.

Evaluating Explainability: A Framework for Systematic Assessment and Reporting of Explainable AI Features Notions of explainability and evaluation approaches for explainable artificial intelligence.Information Fusion, 76:89–106, 2021

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:30:23.805972Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Pith citing papers

Observation e2fa528f-3f59-4907-b15a-7221bb498f57 · inbound

Quantifying Explanation Consistency: The C-Score Metric for CAM-Based Explainability in Medical Image Classification cites this paper.

Quantifying Explanation Consistency: The C-Score Metric for CAM-Based Explainability in Medical Image Classification Evaluating Explainability: A Framework for Systematic Assessment and Reporting of Explainable AI Features

Reference 39

Resolution: verified exact; arxiv_id, observed 2026-05-11T00:45:49.512200Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.