Paper Citation Record · LEDGER

The Explabox: Model-Agnostic Machine Learning Transparency & Analysis

As of 13 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 0 inbound Pith citation observations for arXiv:2411.15257.

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

pith.paper-citation-record.v1
2411.15257 v1

Coverage vector

measured 23 of 23 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T14:55:03.537084Z

measured 23 of 23 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 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

23 of 23 outbound references displayed

verified exact4
verified fuzzy13
unresolved6
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 70fc0db3-40b3-4cc9-96dc-bda8f07c8440 · outbound

This paper cites Fair Regression: Quantitative Definitions and Reduction-Based Algorithms.

The Explabox: Model-Agnostic Machine Learning Transparency & Analysis Fair Regression: Quantitative Definitions and Reduction-Based Algorithms

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:55:04.080788Z

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.

Observation 26107b18-85b4-4814-aea0-9cef37357b98 · outbound

This paper cites One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques.

The Explabox: Model-Agnostic Machine Learning Transparency & Analysis One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-12T14:55:03.399031Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6dedbe9c-eb30-41c2-ae9d-8f7a609dad4d · outbound

This paper cites dalex: Responsible Machine Learning with Interactive Explainability and Fairness in Python.

The Explabox: Model-Agnostic Machine Learning Transparency & Analysis dalex: Responsible Machine Learning with Interactive Explainability and Fairness in Python

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:55:04.063992Z

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.

Observation 0fab1b14-b961-4ac5-bb17-d49646fbaf68 · outbound

This paper cites AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias.

The Explabox: Model-Agnostic Machine Learning Transparency & Analysis AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-12T14:55:03.410746Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 98885404-5d27-4bd4-9566-d863ae6b1237 · outbound

This paper cites an unresolved cited work.

The Explabox: Model-Agnostic Machine Learning Transparency & Analysis Unresolved cited work

Reference 5

Resolution: verified exact; doi, observed 2026-08-12T14:55:03.640025Z

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.

Observation 80caaf93-e3db-40d7-97fd-41d1e48c1685 · outbound

This paper cites The EU AI Act: A summary of its significance and scope.

The Explabox: Model-Agnostic Machine Learning Transparency & Analysis The EU AI Act: A summary of its significance and scope

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:55:04.046847Z

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.

Observation 8dffc5e3-c600-4efa-b523-352866bec94b · outbound

This paper cites Python package Faker , 2021.

The Explabox: Model-Agnostic Machine Learning Transparency & Analysis Python package Faker , 2021

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:55:04.028392Z

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.

Observation 2e559de5-ef9d-481e-8f30-7a354d5af77c · outbound

This paper cites Local Rule-Based Explanations of Black Box Decision Systems.

The Explabox: Model-Agnostic Machine Learning Transparency & Analysis Local Rule-Based Explanations of Black Box Decision Systems

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-12T14:55:03.436781Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0d6688db-9b15-42b7-9afe-dc6c0c52d22f · outbound

This paper cites Examples are not Enough, Learn to Criticize! Criticism for Interpretability.

The Explabox: Model-Agnostic Machine Learning Transparency & Analysis Examples are not Enough, Learn to Criticize! Criticism for Interpretability

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:55:04.011816Z

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.

Observation b9f2aac6-f9e0-4afa-a59f-995bc316f7b8 · outbound

This paper cites Alibi Explain: Algorithms for Explaining Machine Learning Models.

The Explabox: Model-Agnostic Machine Learning Transparency & Analysis Alibi Explain: Algorithms for Explaining Machine Learning Models

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:55:03.994526Z

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.

Observation 153693d5-93ee-4337-bc8a-c258a6c40712 · outbound

This paper cites Lundberg and Su-In Lee.

The Explabox: Model-Agnostic Machine Learning Transparency & Analysis Lundberg and Su-In Lee

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:55:03.977181Z

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.

Observation 728f6545-3963-4304-bd65-ec55b143c65a · outbound

This paper cites A Survey on Bias and Fairness in Machine Learning.

The Explabox: Model-Agnostic Machine Learning Transparency & Analysis A Survey on Bias and Fairness in Machine Learning

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:55:03.959702Z

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.

Observation 16cc6cf2-e5ed-439a-bbe0-84aad6ed50d0 · outbound

This paper cites Pedregosa, G.

The Explabox: Model-Agnostic Machine Learning Transparency & Analysis Pedregosa, G

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-12T14:55:03.478424Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2e7f0f96-9928-4595-bf32-e15a526bcb66 · outbound

This paper cites Collaborative data science, 2015.

The Explabox: Model-Agnostic Machine Learning Transparency & Analysis Collaborative data science, 2015

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-12T14:55:03.484457Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 35d8ca74-8726-40b8-9b18-8b161ee43448 · outbound

This paper cites ``Why Should I Trust You?'': Explaining the Predictions of Any Classifier.

The Explabox: Model-Agnostic Machine Learning Transparency & Analysis ``Why Should I Trust You?'': Explaining the Predictions of Any Classifier

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:55:03.916303Z

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.

Observation 03fd94dd-2592-452a-8d76-969c944d6f11 · outbound

This paper cites Model-Agnostic Interpretability of Machine Learning.

The Explabox: Model-Agnostic Machine Learning Transparency & Analysis Model-Agnostic Interpretability of Machine Learning

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-12T14:55:03.497062Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 96d22dd5-2f21-40ad-b3c1-d20eccd73a87 · outbound

This paper cites Anchors: High-Precision Model-Agnostic Explanations.

The Explabox: Model-Agnostic Machine Learning Transparency & Analysis Anchors: High-Precision Model-Agnostic Explanations

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:55:03.898674Z

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.

Observation 47c6d8ff-9d93-4239-8864-fd03e62e6e42 · outbound

This paper cites Beyond Accuracy: Behavioral Testing of NLP models with CheckList.

The Explabox: Model-Agnostic Machine Learning Transparency & Analysis Beyond Accuracy: Behavioral Testing of NLP models with CheckList

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:55:03.881364Z

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.

Observation d5d53814-f15c-4a56-a943-7a5e24466036 · outbound

This paper cites Python package text\_explainability, 2021 a.

The Explabox: Model-Agnostic Machine Learning Transparency & Analysis Python package text\_explainability, 2021 a

Reference 19

Resolution: verified exact; doi, observed 2026-08-12T14:55:03.621945Z

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.

Observation f0e7cd72-3e5d-407d-8d6e-e7b8b77183fa · outbound

This paper cites Python package text\_sensitivity, 2021 b.

The Explabox: Model-Agnostic Machine Learning Transparency & Analysis Python package text\_sensitivity, 2021 b

Reference 20

Resolution: verified exact; doi, observed 2026-08-12T14:55:03.602665Z

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.

Observation 7f03965e-4fa8-471a-853f-8263b8fe266e · outbound

This paper cites imodels: A python package for fitting interpretable models.

The Explabox: Model-Agnostic Machine Learning Transparency & Analysis imodels: A python package for fitting interpretable models

Reference 21

Resolution: verified exact; doi, observed 2026-08-12T14:55:03.581242Z

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.

Observation a72f1aa5-6f0e-4542-a3ce-5255196f177f · outbound

This paper cites Interpretable to Whom? A Role-based Model for Analyzing Interpretable Machine Learning Systems.

The Explabox: Model-Agnostic Machine Learning Transparency & Analysis Interpretable to Whom? A Role-based Model for Analyzing Interpretable Machine Learning Systems

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:55:03.861520Z

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.

Observation f6c6267a-06a8-4f49-8927-31bdab4a5f8f · outbound

This paper cites Contrastive Explanations with Local Foil Trees.

The Explabox: Model-Agnostic Machine Learning Transparency & Analysis Contrastive Explanations with Local Foil Trees

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:55:03.842590Z

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.

Pith citing papers

No inbound Pith citation observations are available.