Paper Citation Record · LEDGER

Reconciling Privacy and Explainability in High-Stakes: A Systematic Inquiry

As of 11 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 1 inbound Pith citation observation for arXiv:2412.20798.

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

pith.paper-citation-record.v1
2412.20798 v4

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T23:15:09.342436Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-01T01:57:54.065453Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T12:35:43.853867Z

Reference resolution

39 of 39 outbound references displayed

verified exact3
verified fuzzy7
unresolved28
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 12d07bff-6923-4fbf-90f3-fa0185394ce4 · outbound

This paper cites Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang.

Reconciling Privacy and Explainability in High-Stakes: A Systematic Inquiry Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:15:10.365511Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 70cb0e54-3b1f-41a2-899a-950fd6838ca0 · outbound

This paper cites Explaining Image Classifiers by Counterfactual Generation.

Reconciling Privacy and Explainability in High-Stakes: A Systematic Inquiry Explaining Image Classifiers by Counterfactual Generation

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-10T23:15:09.186843Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 47472c71-ae80-48a9-a5b9-6234199b2f33 · outbound

This paper cites Reliability of CKA as a Similarity Measure in Deep Learning.

Reconciling Privacy and Explainability in High-Stakes: A Systematic Inquiry Reliability of CKA as a Similarity Measure in Deep Learning

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-10T23:15:09.191380Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eb0cb6cf-1ad0-4480-b7d1-75bd58ebd0a3 · outbound

This paper cites Differential Privacy and Fairness in Decisions and Learning Tasks: A Survey.

Reconciling Privacy and Explainability in High-Stakes: A Systematic Inquiry Differential Privacy and Fairness in Decisions and Learning Tasks: A Survey

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-10T23:15:09.202907Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 62637243-09c2-425d-b942-694d8216c06b · outbound

This paper cites Explainable Lung Disease Classification from Chest X-Ray Images Utilizing Deep Learning and XAI.

Reconciling Privacy and Explainability in High-Stakes: A Systematic Inquiry Explainable Lung Disease Classification from Chest X-Ray Images Utilizing Deep Learning and XAI

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-10T23:15:09.213339Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a55df274-acde-49ce-8b9a-31ca6e593496 · outbound

This paper cites Towards Faithfully Interpretable NLP Systems: How should we define and evaluate faithfulness?.

Reconciling Privacy and Explainability in High-Stakes: A Systematic Inquiry Towards Faithfully Interpretable NLP Systems: How should we define and evaluate faithfulness?

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-10T23:15:09.217779Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0a3b6821-704e-409b-9f91-c20df7f5631b · outbound

This paper cites Investigating sanity checks for saliency maps with image and text classification.

Reconciling Privacy and Explainability in High-Stakes: A Systematic Inquiry Investigating sanity checks for saliency maps with image and text classification

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-10T23:15:09.227211Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1acff20f-cac3-4a86-ab7b-39b70b9bad11 · outbound

This paper cites The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective.

Reconciling Privacy and Explainability in High-Stakes: A Systematic Inquiry The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-10T23:15:09.232108Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 828b3531-ea5b-4895-9a93-b151fd055a1c · outbound

This paper cites Why Does Differential Privacy with Large Epsilon Defend Against Practical Membership Inference Attacks?.

Reconciling Privacy and Explainability in High-Stakes: A Systematic Inquiry Why Does Differential Privacy with Large Epsilon Defend Against Practical Membership Inference Attacks?

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-10T23:15:09.236809Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 09c45bf5-5636-4609-9dc1-fb9278d924bd · outbound

This paper cites Robust counterfactual explanations for privacy-preserving svm.

Reconciling Privacy and Explainability in High-Stakes: A Systematic Inquiry Robust counterfactual explanations for privacy-preserving svm

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:15:10.333395Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 87b6d312-10c9-43b1-b1c9-f2e411f5950e · outbound

This paper cites doi: https://doi.org/10.1016/j.ejc.2022.103515.

Reconciling Privacy and Explainability in High-Stakes: A Systematic Inquiry doi: https://doi.org/10.1016/j.ejc.2022.103515

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-10T23:15:09.247824Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 67b69f3b-3d7b-41ac-99f0-29efb10e7105 · outbound

This paper cites On quantitative aspects of model interpretability.

Reconciling Privacy and Explainability in High-Stakes: A Systematic Inquiry On quantitative aspects of model interpretability

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-10T23:15:09.253205Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8e24d0e3-ed56-44ac-ac78-b09bbc81347a · outbound

This paper cites A Survey of Privacy-Preserving Model Explanations: Privacy Risks, Attacks, and Countermeasures.

Reconciling Privacy and Explainability in High-Stakes: A Systematic Inquiry A Survey of Privacy-Preserving Model Explanations: Privacy Risks, Attacks, and Countermeasures

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-10T23:15:09.258133Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8b9b2b53-ff72-4a15-a991-99b67888306e · outbound

This paper cites Scalable Private Learning with PATE.

Reconciling Privacy and Explainability in High-Stakes: A Systematic Inquiry Scalable Private Learning with PATE

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-10T23:15:09.263044Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 083df469-f5b0-4ee7-a8b9-e0f5e3df49a5 · outbound

This paper cites RISE: Randomized Input Sampling for Explanation of Black-box Models.

Reconciling Privacy and Explainability in High-Stakes: A Systematic Inquiry RISE: Randomized Input Sampling for Explanation of Black-box Models

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-10T23:15:09.268240Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ab50c3da-8713-4715-92c2-360e0996e52c · outbound

This paper cites doi: 10.1613/jair.1.14649.

Reconciling Privacy and Explainability in High-Stakes: A Systematic Inquiry doi: 10.1613/jair.1.14649

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-10T23:15:09.273570Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0f0a6c34-ed61-462e-a564-5bbfb716e4c5 · outbound

This paper cites IROF: a low resource evaluation metric for explanation methods.

Reconciling Privacy and Explainability in High-Stakes: A Systematic Inquiry IROF: a low resource evaluation metric for explanation methods

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-10T23:15:09.278521Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 82b81862-c2b5-47ce-92b4-6a7695cdb25d · outbound

This paper cites doi: 10.1145/3624010.

Reconciling Privacy and Explainability in High-Stakes: A Systematic Inquiry doi: 10.1145/3624010

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-10T23:15:09.283454Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 42ead904-5233-45f0-9428-47227c369c5e · outbound

This paper cites On the privacy risks of model explanations.

Reconciling Privacy and Explainability in High-Stakes: A Systematic Inquiry On the privacy risks of model explanations

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:15:10.318760Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation b8a466f8-0692-48dd-9357-f7e9f0cf1497 · outbound

This paper cites SmoothGrad: removing noise by adding noise.

Reconciling Privacy and Explainability in High-Stakes: A Systematic Inquiry SmoothGrad: removing noise by adding noise

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-10T23:15:09.302558Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 903898a1-4e96-4b2f-b399-fb67e10509b6 · outbound

This paper cites Chasing your long tails: Differentially private prediction in health care settings.

Reconciling Privacy and Explainability in High-Stakes: A Systematic Inquiry Chasing your long tails: Differentially private prediction in health care settings

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:15:10.303499Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c2598ad6-06d3-44eb-8b44-77e37766c269 · outbound

This paper cites Revisiting Sanity Checks for Saliency Maps.

Reconciling Privacy and Explainability in High-Stakes: A Systematic Inquiry Revisiting Sanity Checks for Saliency Maps

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-10T23:15:09.320191Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation df1406aa-efc0-4b2d-91ca-20a2d1274b4f · outbound

This paper cites Opacus: User-Friendly Differential Privacy Library in PyTorch.

Reconciling Privacy and Explainability in High-Stakes: A Systematic Inquiry Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-10T23:15:09.324499Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 27d68f07-cee4-4007-808e-377373b14010 · outbound

This paper cites Revisiting Privacy-Utility Trade-off for DP Training with Pre-existing Knowledge.

Reconciling Privacy and Explainability in High-Stakes: A Systematic Inquiry Revisiting Privacy-Utility Trade-off for DP Training with Pre-existing Knowledge

Reference 36

Resolution: verified exact; local_arxiv, observed 2026-08-10T23:15:09.456956Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation f5ca3be6-f7eb-4894-9654-c4ebb1f9d25e · outbound

This paper cites Open-world machine learning: A review and new outlooks.

Reconciling Privacy and Explainability in High-Stakes: A Systematic Inquiry Open-world machine learning: A review and new outlooks

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-10T23:15:09.333496Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0006e7ee-75bf-45f2-a3ee-9ad723b6726f · outbound

This paper cites For a given feature map Ak, the weights are computed as: αc k = 1 Z ∑ i ∑ j ∂yc ∂Aij k , where yc is the output score for classc, andZ is the spatial dimensions ofAk.

Reconciling Privacy and Explainability in High-Stakes: A Systematic Inquiry For a given feature map Ak, the weights are computed as: αc k = 1 Z ∑ i ∑ j ∂yc ∂Aij k , where yc is the output score for classc, andZ is the spatial dimensions ofAk

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:15:10.287249Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 4d40361e-dcf3-4b10-b7b6-45e06c2d86c6 · outbound

This paper cites From the engineering perspective, we have to select one such replacement that scales with sufficiently large datasets without hampering the privacy bounds.

Reconciling Privacy and Explainability in High-Stakes: A Systematic Inquiry From the engineering perspective, we have to select one such replacement that scales with sufficiently large datasets without hampering the privacy bounds

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:15:10.270141Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 29a18316-e71d-43b0-bca9-0d71e35ff3f5 · outbound

This paper cites Fan Yang, Qizhang Feng, Kaixiong Zhou, Jiahao Chen, and Xia Hu.

Reconciling Privacy and Explainability in High-Stakes: A Systematic Inquiry Fan Yang, Qizhang Feng, Kaixiong Zhou, Jiahao Chen, and Xia Hu

Reference 1967

Resolution: unresolved; no resolver link, observed 2026-08-10T23:15:09.316068Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 93a5b590-c046-4e73-8487-d2e0fc6d89c4 · outbound

This paper cites Robustness Threats of Differential Privacy.

Reconciling Privacy and Explainability in High-Stakes: A Systematic Inquiry Robustness Threats of Differential Privacy

Reference 1975

Resolution: unresolved; no resolver link, observed 2026-08-10T23:15:09.311620Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a27f4c9f-5828-4549-b74b-b60f53aa032f · outbound

This paper cites Towards efficient and scalable training of differentially private deep learning.arXiv preprint arXiv:2406.17298,.

Reconciling Privacy and Explainability in High-Stakes: A Systematic Inquiry Towards efficient and scalable training of differentially private deep learning.arXiv preprint arXiv:2406.17298,

Reference 2015

Resolution: verified exact; raw_fallback, observed 2026-08-10T23:15:10.138646Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 9f35735f-23ab-45e2-b8f4-c7a738c67eef · outbound

This paper cites URL http://dx.doi.org/10.1145/2976749.2978318.

Reconciling Privacy and Explainability in High-Stakes: A Systematic Inquiry URL http://dx.doi.org/10.1145/2976749.2978318

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-10T23:15:09.162381Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e9d74322-f12c-4b85-bbb2-c8e7085c2af0 · outbound

This paper cites Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models.

Reconciling Privacy and Explainability in High-Stakes: A Systematic Inquiry Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-10T23:15:09.288424Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e878b8ed-85b6-49df-8156-e7e27daa857f · outbound

This paper cites On the Robustness of Interpretability Methods.

Reconciling Privacy and Explainability in High-Stakes: A Systematic Inquiry On the Robustness of Interpretability Methods

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-10T23:15:09.167799Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ec0eb78e-df6b-443a-a49c-e9e5155c7194 · outbound

This paper cites doi: 10.1007/s11263-019-01228-7.

Reconciling Privacy and Explainability in High-Stakes: A Systematic Inquiry doi: 10.1007/s11263-019-01228-7

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-10T23:15:09.293359Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 17099beb-1461-4323-b78b-c0f65074e7db · outbound

This paper cites Explainable Machine Learning in Deployment.

Reconciling Privacy and Explainability in High-Stakes: A Systematic Inquiry Explainable Machine Learning in Deployment

Reference 2020

Resolution: metadata mismatch; local_arxiv, observed 2026-08-10T23:15:10.034230Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 1d71b81c-35a1-43f3-9b34-1dc763e82ddf · outbound

This paper cites On Baselines for Local Feature Attributions.

Reconciling Privacy and Explainability in High-Stakes: A Systematic Inquiry On Baselines for Local Feature Attributions

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-10T23:15:09.208144Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 499c2c55-c21a-4e87-9a9e-2cefe452cc5e · outbound

This paper cites doi: 10.1145/3547139.

Reconciling Privacy and Explainability in High-Stakes: A Systematic Inquiry doi: 10.1145/3547139

Reference 2022

Resolution: verified exact; doi, observed 2026-08-10T23:15:09.415183Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e687832c-1d29-4b78-ad6b-a209da229f8a · outbound

This paper cites Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory.

Reconciling Privacy and Explainability in High-Stakes: A Systematic Inquiry Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-10T23:15:09.222527Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ef34dcb4-109a-4c6f-ac36-061329c1c7d7 · outbound

This paper cites Image pixelization with differential privacy.

Reconciling Privacy and Explainability in High-Stakes: A Systematic Inquiry Image pixelization with differential privacy

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:15:10.350153Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Pith citing papers

Observation ea4c7494-77ee-49ec-abb3-ea663d34a013 · inbound

Training Therapeutic Judges and Multi-Agent Systems for Human-Aligned Mental Health Support cites this paper.

Training Therapeutic Judges and Multi-Agent Systems for Human-Aligned Mental Health Support Reconciling Privacy and Explainability in High-Stakes: A Systematic Inquiry

Reference 81

Resolution: verified exact; arxiv_id, observed 2026-07-01T12:35:43.855138Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.