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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 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 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 3 of 3 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T11:51:46.108890Z

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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-05-23T20:21:52.229228Z digest=sha256:d324a49b66dee84935b2f3d18b8230675c5b8ebc9c564005070f8f8e912698c0

Observation 5f9c9733-0550-4842-8bdf-03b17807c3bd · inbound

From Point to probabilistic gradient boosting for claim frequency and severity prediction cites this paper.

From Point to probabilistic gradient boosting for claim frequency and severity prediction Why You Should Not Trust Interpretations in Machine Learning: Adversarial Attacks on Partial Dependence Plots

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-11T11:51:46.108890Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T11:51:46.108890Z digest=sha256:1c666d74550b149bce51048065994f35a581c3a273b50b1ea64b7229563c06d0

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:d2f0a6c460a11581218ddeea179f57045203004859793b673354e4305f9b2199