Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 16 inbound Pith citation observations for arXiv:2202.01602.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T12:23:07.164904Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z
0 of 0 outbound references displayed
External citation measurements
43
arxiv_reference, observed 2026-08-05T02:28:24.338817Z
No outbound reference observations are available for this paper version.
Observation fc33f939-5319-4002-9284-4208845dd7ac · inbound
Multi-criteria Rank-based Aggregation for Explainable AI The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective
Reference 10
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7f91b1f7-9335-41fb-a897-3ea38f3a93b2 · inbound
CASE: Contrastive Activation for Saliency Estimation The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective
Reference 18
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1c0ca5d0-c83e-4962-9252-7ba788777b65 · inbound
Your Model Is Unfair, Are You Even Aware? Inverse Relationship Between Comprehension and Trust in Explainability Visualizations of Biased ML Models The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective
Reference 54
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d8dafae3-665a-48d9-9904-7edc8e64b6f5 · inbound
On Spectral Properties of Gradient-based Explanation Methods The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective
Reference 36
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2c126e81-9d50-4112-bfe3-05195db27861 · inbound
HattriQ: Designing Integrated Gradients for Feature Attribution in Quantum Machine Learning The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective
Reference 8
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3c07c82b-c4f6-4e26-b5b1-ce07d19b5f79 · inbound
Explanation Bias is a Product: Revealing the Hidden Lexical and Position Preferences in Post-Hoc Feature Attribution The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective
Reference 3
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 47a16528-800c-43ef-9608-dd8816393d12 · inbound
Interpretable Text Classification Applied to the Detection of LLM-generated Creative Writing The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective
Reference 1
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a60d5784-53a2-4e28-babe-3ac9b78db03f · inbound
Quantifying Explanation Consistency: The C-Score Metric for CAM-Based Explainability in Medical Image Classification The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective
Reference 20
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 74a2a549-df3a-4e9f-a2fa-f233a52daa32 · inbound
ToxiTrace: Gradient-Aligned Training for Explainable Chinese Toxicity Detection The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective
Reference 2
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 7cbe9ac7-5d48-4786-a7a3-49abdef7964a · inbound
Interpretable and Explainable Surrogate Modeling for Simulations: A State-of-the-Art Survey and Perspectives on Explainable AI for Decision-Making The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective
Reference 223
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation fb8b5ab0-092e-4103-a712-4768fc1fe4aa · inbound
Persistent and Conversational Multi-Method Explainability for Trustworthy Financial AI The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective
Reference 11
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 9dee54c4-8b5b-4490-8e5d-72da84b73de2 · inbound
The Attribution Impossibility: No Feature Ranking Is Faithful, Stable, and Complete Under Collinearity The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective
Reference 67
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 73845411-c58f-4c71-b8f7-a8edbb43d214 · inbound
LLM Doesn't Know What It Doesn't Know: Detecting Epistemic Blind Spots via Cross-Model Attribution Divergence on Clinical Tabular Data The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective
Reference 7
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation ff5509f6-a014-4b20-b323-e61a24814cb6 · inbound
Position: Explainability Research Must Prioritize Foundations over Ad-hoc Methods The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective
Reference 19
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f9958957-2f9f-4fac-9384-5d6f4debad8d · inbound
Circuit Claims Depend on What Is Extracted and How It Is Compared The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective
Reference 21
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 104a10ec-8bbc-4a85-8ed2-d641628bd570 · inbound
Explainable AI for the EU Right to Explanation: A Systematic Review of the Law-XAI Translation Gap The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective
Reference 28
Source-reported events for the cited work
Unavailable: canonical work link unavailable.