Paper Citation Record · LEDGER

Interpretable to Whom? A Role-based Model for Analyzing Interpretable Machine Learning Systems

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:1806.07552.

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

pith.paper-citation-record.v1
1806.07552 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:15:41.997043Z

measured 1 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

85
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 04cf7efb-06ec-4d51-87c5-088287c35573 · inbound

Are machine learning interpretations reliable? A stability study on global interpretations cites this paper.

Are machine learning interpretations reliable? A stability study on global interpretations Interpretable to Whom? A Role-based Model for Analyzing Interpretable Machine Learning Systems

Reference 122

Resolution: unresolved; no resolver link, observed 2026-08-07T15:15:41.997043Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 27892ac6-68a6-4049-b745-490ded9fc128 · inbound

Explainable AI Systems Must Be Contestable: Here's How to Make It Happen cites this paper.

Explainable AI Systems Must Be Contestable: Here's How to Make It Happen Interpretable to Whom? A Role-based Model for Analyzing Interpretable Machine Learning Systems

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-07T11:44:12.259714Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f341a533-9546-4baf-b4ea-4349986af679 · inbound

A Taxonomy for Design and Evaluation of Prompt-Based Natural Language Explanations cites this paper.

A Taxonomy for Design and Evaluation of Prompt-Based Natural Language Explanations Interpretable to Whom? A Role-based Model for Analyzing Interpretable Machine Learning Systems

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-06T18:19:58.892829Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f719ad69-cd5e-4e59-a662-c78e8a48d54c · inbound

The 2025 AI Agent Index: Documenting Technical and Safety Features of Deployed Agentic AI Systems cites this paper.

The 2025 AI Agent Index: Documenting Technical and Safety Features of Deployed Agentic AI Systems Interpretable to Whom? A Role-based Model for Analyzing Interpretable Machine Learning Systems

Reference 122

Resolution: verified exact; local_arxiv, observed 2026-05-15T20:46:35.615343Z

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.