Paper Citation Record · LEDGER

MetaExplainer: A Framework to Generate Multi-Type User-Centered Explanations for AI Systems

As of 19 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 0 inbound Pith citation observations for arXiv:2508.00300.

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

pith.paper-citation-record.v1
2508.00300 v2

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T10:18:07.182643Z

measured 38 of 38 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+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

38 of 38 outbound references displayed

verified exact0
verified fuzzy30
unresolved8
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 25d7abbf-fe82-49e5-8471-0d97e262d39a · outbound

This paper cites Selecting predicate logic for knowledge representation by comparative study of knowledge representation schemes.

MetaExplainer: A Framework to Generate Multi-Type User-Centered Explanations for AI Systems Selecting predicate logic for knowledge representation by comparative study of knowledge representation schemes

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:18:07.907105Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 84466162-526d-4130-8f5d-40d3178512c4 · outbound

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

MetaExplainer: A Framework to Generate Multi-Type User-Centered Explanations for AI Systems One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques

Reference 2

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9b583a8a-818f-4e20-8558-8fd25dc51c34 · outbound

This paper cites Ai explainability 360: Impact and design.

MetaExplainer: A Framework to Generate Multi-Type User-Centered Explanations for AI Systems Ai explainability 360: Impact and design

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:18:07.891762Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6dcc9396-8381-4fe3-8f24-0c17f7dced39 · outbound

This paper cites Pima indians diabetes mellitus classification based on machine learning (ml) algorithms.

MetaExplainer: A Framework to Generate Multi-Type User-Centered Explanations for AI Systems Pima indians diabetes mellitus classification based on machine learning (ml) algorithms

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:18:07.873727Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a858ecc1-7122-4552-9eb8-579738748131 · outbound

This paper cites Informing clinical assessment by contextualizing post-hoc explanations of risk prediction models in type-2 diabetes.

MetaExplainer: A Framework to Generate Multi-Type User-Centered Explanations for AI Systems Informing clinical assessment by contextualizing post-hoc explanations of risk prediction models in type-2 diabetes

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:18:07.853781Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3bd85aa4-98c0-46d2-9bf3-0fc664c45511 · outbound

This paper cites Explanation ontology: A general-purpose, semantic representation for supporting user-centered explanations.

MetaExplainer: A Framework to Generate Multi-Type User-Centered Explanations for AI Systems Explanation ontology: A general-purpose, semantic representation for supporting user-centered explanations

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:18:07.838864Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7b1fb254-de67-4181-ad11-9ad7d063dca5 · outbound

This paper cites Boolean decision rules via column generation.

MetaExplainer: A Framework to Generate Multi-Type User-Centered Explanations for AI Systems Boolean decision rules via column generation

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:18:07.824034Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b63a43b3-5b25-41c8-b099-264a6bb3d5ad · outbound

This paper cites Human-centered explainability for life sciences, healthcare, and medical informatics.

MetaExplainer: A Framework to Generate Multi-Type User-Centered Explanations for AI Systems Human-centered explainability for life sciences, healthcare, and medical informatics

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:18:07.808908Z

Source-reported events for the cited work

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Observation 2d3f811b-393e-43d2-9c3a-c48d2bea28ce · outbound

This paper cites Accountability of AI Under the Law: The Role of Explanation.

MetaExplainer: A Framework to Generate Multi-Type User-Centered Explanations for AI Systems Accountability of AI Under the Law: The Role of Explanation

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T10:18:07.042556Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 91bbef21-4921-4520-8e8c-bc9c8253f8d5 · outbound

This paper cites Ragas: Automated Evaluation of Retrieval Augmented Generation.

MetaExplainer: A Framework to Generate Multi-Type User-Centered Explanations for AI Systems Ragas: Automated Evaluation of Retrieval Augmented Generation

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T10:18:07.046764Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6ffe6548-4488-4b85-8116-8db85a70b12f · outbound

This paper cites Retrieval-Augmented Generation for Large Language Models: A Survey.

MetaExplainer: A Framework to Generate Multi-Type User-Centered Explanations for AI Systems Retrieval-Augmented Generation for Large Language Models: A Survey

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T10:18:07.050887Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a7e87a55-24a2-4a1c-a74d-14fb0aee3c1e · outbound

This paper cites The false hope of current approaches to explainable artificial intelligence in health care.

MetaExplainer: A Framework to Generate Multi-Type User-Centered Explanations for AI Systems The false hope of current approaches to explainable artificial intelligence in health care

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:18:07.795453Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 63fd702d-de2e-419c-a936-012290280a2c · outbound

This paper cites Designing for ai explainability in clinical context.

MetaExplainer: A Framework to Generate Multi-Type User-Centered Explanations for AI Systems Designing for ai explainability in clinical context

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:18:07.777756Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3e6d0f81-d9f6-4bb2-be44-24da7d0876d4 · outbound

This paper cites Supervised Contrastive Learning for Pre-trained Language Model Fine-tuning.

MetaExplainer: A Framework to Generate Multi-Type User-Centered Explanations for AI Systems Supervised Contrastive Learning for Pre-trained Language Model Fine-tuning

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T10:18:07.068592Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 92b449c2-d89d-4776-bcd9-c2ab1c03bbfb · outbound

This paper cites Metrics for Explainable AI: Challenges and Prospects.

MetaExplainer: A Framework to Generate Multi-Type User-Centered Explanations for AI Systems Metrics for Explainable AI: Challenges and Prospects

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T10:18:07.073100Z

Source-reported events for the cited work

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Observation 09a92eed-fd3c-42a6-afc1-a141f887f2f4 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

MetaExplainer: A Framework to Generate Multi-Type User-Centered Explanations for AI Systems LoRA: Low-Rank Adaptation of Large Language Models

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T10:18:07.077865Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 20fc1dc5-59b0-4658-aa03-ea01d08921ab · outbound

This paper cites Towards bridging the gaps between the right to explanation and the right to be forgotten.

MetaExplainer: A Framework to Generate Multi-Type User-Centered Explanations for AI Systems Towards bridging the gaps between the right to explanation and the right to be forgotten

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:18:07.750746Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ebf91207-6e61-4f6f-be6b-f36985530d1c · outbound

This paper cites Rethinking Explainability as a Dialogue: A Practitioner's Perspective.

MetaExplainer: A Framework to Generate Multi-Type User-Centered Explanations for AI Systems Rethinking Explainability as a Dialogue: A Practitioner's Perspective

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T10:18:07.087637Z

Source-reported events for the cited work

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Observation 19f17bcd-d363-49c7-bd20-0cb7b3d3fce6 · outbound

This paper cites u ttler, Mike Lewis, Wen-tau Yih, Tim Rockt \.

MetaExplainer: A Framework to Generate Multi-Type User-Centered Explanations for AI Systems u ttler, Mike Lewis, Wen-tau Yih, Tim Rockt \

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:18:07.733572Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4bbb8e95-e501-43c0-be33-ff93fe202fa4 · outbound

This paper cites Questioning the ai: informing design practices for explainable ai user experiences.

MetaExplainer: A Framework to Generate Multi-Type User-Centered Explanations for AI Systems Questioning the ai: informing design practices for explainable ai user experiences

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:18:07.716215Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 65ecdd5f-abc3-47ed-b351-369eeac385bb · outbound

This paper cites Connecting algorithmic research and usage contexts: A perspective of contextualized evaluation for explainable ai.

MetaExplainer: A Framework to Generate Multi-Type User-Centered Explanations for AI Systems Connecting algorithmic research and usage contexts: A perspective of contextualized evaluation for explainable ai

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:18:07.702251Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d873a14f-2b68-49d3-a856-6ccbfb0db6eb · outbound

This paper cites Llamaindex.

MetaExplainer: A Framework to Generate Multi-Type User-Centered Explanations for AI Systems Llamaindex

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:18:07.686029Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1a07f370-0a7a-450a-9404-f6ee3b329774 · outbound

This paper cites A unified approach to interpreting model predictions.

MetaExplainer: A Framework to Generate Multi-Type User-Centered Explanations for AI Systems A unified approach to interpreting model predictions

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:18:07.662127Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e783db09-4149-4057-8308-424930e98bb0 · outbound

This paper cites Explaining answers from the semantic web: The inference web approach.

MetaExplainer: A Framework to Generate Multi-Type User-Centered Explanations for AI Systems Explaining answers from the semantic web: The inference web approach

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:18:07.642082Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 78ec73ca-cc8b-4c10-8c3e-60f6e88084bb · outbound

This paper cites Explaining task processing in cognitive assistants that learn.

MetaExplainer: A Framework to Generate Multi-Type User-Centered Explanations for AI Systems Explaining task processing in cognitive assistants that learn

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:18:07.621616Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d562300a-dfc3-45b1-b766-0f3cdde5eddb · outbound

This paper cites Explanation in artificial intelligence: Insights from the social sciences.

MetaExplainer: A Framework to Generate Multi-Type User-Centered Explanations for AI Systems Explanation in artificial intelligence: Insights from the social sciences

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:18:07.601299Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f12f2645-6394-4aa3-92e9-0c2fab880124 · outbound

This paper cites Explaining explanations in ai.

MetaExplainer: A Framework to Generate Multi-Type User-Centered Explanations for AI Systems Explaining explanations in ai

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:18:07.583769Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8f5e28d9-2ae1-4ced-9b8a-e91f6094d841 · outbound

This paper cites Explaining machine learning classifiers through diverse counterfactual explanations.

MetaExplainer: A Framework to Generate Multi-Type User-Centered Explanations for AI Systems Explaining machine learning classifiers through diverse counterfactual explanations

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:18:07.545559Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ae842343-bbae-475c-bfd0-22eddcf0e0cd · outbound

This paper cites Why should i trust you?: Explaining the predictions of any classifier.

MetaExplainer: A Framework to Generate Multi-Type User-Centered Explanations for AI Systems Why should i trust you?: Explaining the predictions of any classifier

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:18:07.529059Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c58c9076-c039-4367-be61-07ac5b532d86 · outbound

This paper cites Evaluating large language models in semantic parsing for conversational question answering over knowledge graphs.

MetaExplainer: A Framework to Generate Multi-Type User-Centered Explanations for AI Systems Evaluating large language models in semantic parsing for conversational question answering over knowledge graphs

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:18:07.510993Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e6141735-4e36-4500-84b0-429b2f196e7c · outbound

This paper cites Explaining machine learning models with interactive natural language conversations using talktomodel.

MetaExplainer: A Framework to Generate Multi-Type User-Centered Explanations for AI Systems Explaining machine learning models with interactive natural language conversations using talktomodel

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:18:07.491284Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 006225c0-45c2-45f9-9f55-f9512ed14a78 · outbound

This paper cites Using the adap learning algorithm to forecast the onset of diabetes mellitus.

MetaExplainer: A Framework to Generate Multi-Type User-Centered Explanations for AI Systems Using the adap learning algorithm to forecast the onset of diabetes mellitus

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:18:07.469784Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9c7fb078-fd00-4384-8790-e757945449a9 · outbound

This paper cites Stanford alpaca: An instruction-following llama model.

MetaExplainer: A Framework to Generate Multi-Type User-Centered Explanations for AI Systems Stanford alpaca: An instruction-following llama model

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:18:07.452049Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e76827d2-83ec-462d-9df8-12d9548dc865 · outbound

This paper cites What clinicians want: contextualizing explainable machine learning for clinical end use.

MetaExplainer: A Framework to Generate Multi-Type User-Centered Explanations for AI Systems What clinicians want: contextualizing explainable machine learning for clinical end use

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:18:07.434408Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4f81a29e-27a6-4e35-ada1-41440a748cf9 · outbound

This paper cites Evaluating xai: A comparison of rule-based and example-based explanations.

MetaExplainer: A Framework to Generate Multi-Type User-Centered Explanations for AI Systems Evaluating xai: A comparison of rule-based and example-based explanations

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:18:07.419128Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation cf8eca35-300e-4e7f-94d2-97a2c6bf63c8 · outbound

This paper cites Counterfactual explanations without opening the black box: Automated decisions and the gdpr.

MetaExplainer: A Framework to Generate Multi-Type User-Centered Explanations for AI Systems Counterfactual explanations without opening the black box: Automated decisions and the gdpr

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:18:07.399626Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1357f7f0-6751-4020-a8a5-5c078acc2a9d · outbound

This paper cites Designing theory-driven user-centric explainable ai.

MetaExplainer: A Framework to Generate Multi-Type User-Centered Explanations for AI Systems Designing theory-driven user-centric explainable ai

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:18:07.384694Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d96fd9ad-deeb-4fc0-a69a-4def4e191e66 · outbound

This paper cites Evaluating the quality of machine learning explanations: A survey on methods and metrics.

MetaExplainer: A Framework to Generate Multi-Type User-Centered Explanations for AI Systems Evaluating the quality of machine learning explanations: A survey on methods and metrics

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:18:07.370057Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

No inbound Pith citation observations are available.