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Paper Citation Record · LEDGER

Why You Should Not Trust Interpretations in Machine Learning: Adversarial Attacks on Partial Dependence Plots

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2404.18702.

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

pith.paper-citation-record.v1
2404.18702 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T00:32:56.074741Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T20:23:24.635709Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation b0ca9e8c-7b73-4173-92e4-d36ccf2d5ebd · inbound

Explainable AI needs formalization cites this paper.

Explainable AI needs formalization Why You Should Not Trust Interpretations in Machine Learning: Adversarial Attacks on Partial Dependence Plots

Reference 13

Resolution
verified exact
arxiv_id, observed 2026-05-23T20:23:24.638927Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-23T20:21:52.229228Z digest=sha256:1b2f6fa8509472f6873dc8fbe6dfeddea2f06861174ed63dd3a9176a67a43ce2

Observation a7675f1c-fc10-49f2-98ff-4beb1e4e0abd · inbound

Beyond Shapley Values: Cooperative Games for the Interpretation of Machine Learning Models cites this paper.

Beyond Shapley Values: Cooperative Games for the Interpretation of Machine Learning Models Why You Should Not Trust Interpretations in Machine Learning: Adversarial Attacks on Partial Dependence Plots

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-07T00:32:56.074741Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:32:56.074741Z digest=sha256:ecae5254e1ef3e383c9b039eb1b42783a79a75cc2cfa0b6e0af7edef42610972