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Paper Citation Record · LEDGER

Machine Learning Explainability for External Stakeholders

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2007.05408.

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

pith.paper-citation-record.v1
2007.05408 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 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 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:03:40.894050Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, 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

40
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation bde50f42-75ec-4681-8d3a-c283b1cb8136 · inbound

Importance of User Control in Data-Centric Steering for Healthcare Experts cites this paper.

Importance of User Control in Data-Centric Steering for Healthcare Experts Machine Learning Explainability for External Stakeholders

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-07T15:03:40.894050Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T15:03:40.894050Z digest=sha256:5b0afa245ea1e8897dd29a7fcd50cf99da6173394b5f1922fb4dcd2394822db3

Observation 86808229-6033-4ff4-b8d1-7eaefe259e80 · inbound

Your Model Is Unfair, Are You Even Aware? Inverse Relationship Between Comprehension and Trust in Explainability Visualizations of Biased ML Models cites this paper.

Your Model Is Unfair, Are You Even Aware? Inverse Relationship Between Comprehension and Trust in Explainability Visualizations of Biased ML Models Machine Learning Explainability for External Stakeholders

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-06T10:25:26.344260Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T10:25:26.344260Z digest=sha256:fc289ba1a88bd3f76409178a6c7ecb79d0bf45377e4e659b447fa4a5ac7001ee

Observation 8c2568d9-8d9a-4cb9-94dc-ccc0e1abadd3 · inbound

Scalable Explainability-as-a-Service (XaaS) for Edge AI Systems cites this paper.

Scalable Explainability-as-a-Service (XaaS) for Edge AI Systems Machine Learning Explainability for External Stakeholders

Reference 14

Resolution
verified exact
arxiv_id, observed 2026-05-16T07:37:32.815920Z

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.

source=pdf_text observed=2026-05-16T07:33:26.604213Z digest=sha256:0b955c85d989e512b1f1015df637b89c0198fbf708422ca70c2adc31a7df23db

Observation ead5844a-2471-4053-9531-f281e1429896 · inbound

Scalable Explainability-as-a-Service (XaaS) for Edge AI Systems cites this paper.

Scalable Explainability-as-a-Service (XaaS) for Edge AI Systems Machine Learning Explainability for External Stakeholders

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-03T04:48:42.417917Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T04:48:42.417917Z digest=sha256:aa7c4d0933f993d9ec344f5fe4c2e1b75fd815571aca5d219200cfa9926a8388

Observation 3e5987e7-0411-4c2b-b844-0186074e53ac · inbound

Exploring CoCo Challenges in ML Engineering Teams: Insights From the Semiconductor Industry cites this paper.

Exploring CoCo Challenges in ML Engineering Teams: Insights From the Semiconductor Industry Machine Learning Explainability for External Stakeholders

Reference 5

Resolution
verified exact
arxiv_id, observed 2026-05-11T01:50:50.816986Z

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.

source=pdf_text observed=2026-05-11T01:48:08.643483Z digest=sha256:9546c1ef009af8982391170514ebc89b6bbf88d0f140553cd3c7e2924ea8dc5a