Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information | Paper Citation Record

Observation a3c325f8-3683-42a2-8552-e9055e2e0f6d · outbound

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

Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information ”why should i trust you?”: Explaining the predictions of any classifier

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This paper cites Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra.

Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra

Reference 2

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This paper cites Cats and Dogs Classification Dataset.https://www.kaggle.com/datasets/ bhavikjikadara/dog-and-cat-classification-dataset, 2024.

Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Cats and Dogs Classification Dataset.https://www.kaggle.com/datasets/ bhavikjikadara/dog-and-cat-classification-dataset, 2024

Reference 3

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This paper cites Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps.

Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

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Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Axiomatic attribution for deep networks

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

Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Counterfactual explanations without opening the black box: Automated decisions and the gdpr.Harv

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This paper cites Mechanistic Interpretability for AI Safety - A Review.Transac- tions on Machine Learning Research, 2024.

Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Mechanistic Interpretability for AI Safety - A Review.Transac- tions on Machine Learning Research, 2024

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This paper cites Lundberg and Su-In Lee.

Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Lundberg and Su-In Lee

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This paper cites Beyond individualized recourse: Interpretable and interac- tive summaries of actionable recourses.

Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Beyond individualized recourse: Interpretable and interac- tive summaries of actionable recourses

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Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Investigating hiring bias in large language models

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Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information SmoothGrad: removing noise by adding noise

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Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Learning important features through propagating activation differences

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

Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Using the adap learning algorithm to forecast the onset of diabetes mellitus

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This paper cites Dropout: A simple way to prevent neural networks from overfitting.Journal of Machine Learning Research, 15(56):1929–1958, 2014.

Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Dropout: A simple way to prevent neural networks from overfitting.Journal of Machine Learning Research, 15(56):1929–1958, 2014

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Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Searching for mobilenetv3

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This paper cites Legal judgment reimagined: PredEx and the rise of intelligent AI interpretation in Indian courts.

Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Legal judgment reimagined: PredEx and the rise of intelligent AI interpretation in Indian courts

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This paper cites Using ”annotator rationales” to improve machine learning for text categorization.

Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Using ”annotator rationales” to improve machine learning for text categorization

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Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Unresolved cited work

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This paper cites Amazon puts its own “brands” first above better-rated products.The Markup, October 2021.

Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Amazon puts its own “brands” first above better-rated products.The Markup, October 2021

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This paper cites Explainable ai in industry: Practical challenges and lessons learned.

Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Explainable ai in industry: Practical challenges and lessons learned

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This paper cites URL https://docs.arize.com/arize/machine-learning/ how-to-ml/explainability/surrogate-model.

Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information URL https://docs.arize.com/arize/machine-learning/ how-to-ml/explainability/surrogate-model

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This paper cites URL https://docs.fiddler.ai/ui-guide/ explainability-ui-giude/surrogate-models.

Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information URL https://docs.fiddler.ai/ui-guide/ explainability-ui-giude/surrogate-models

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This paper cites URL https://learn.microsoft.com/en-us/azure/machine-learning/ concept-model-interpretability.

Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information URL https://learn.microsoft.com/en-us/azure/machine-learning/ concept-model-interpretability

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This paper cites Calmon, and Mario Diaz.

Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Calmon, and Mario Diaz

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Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Amazing things come from having many good models,

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Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Understanding prediction discrepancies in classification.Machine Learning, Aug 2024

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Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Predictive multiplicity in classification

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This paper cites Amazing Things Come From Having Many Good Models.

Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Amazing Things Come From Having Many Good Models

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Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Unresolved cited work

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Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Unresolved cited work

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This paper cites An empirical evaluation of the rashomon effect in explainable machine learning.

Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information An empirical evaluation of the rashomon effect in explainable machine learning

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Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information ISBN 978-3-031-43418-1

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Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Interpretable machine learning as a tool for scientific discovery in chemistry.New J

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Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Unresolved cited work

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Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Duarte, and Jochen Garcke

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Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Esterhuizen, Bryan R

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Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Unresolved cited work

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Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Machine learning- assisted study of ren(x)c(6-x)-doped graphene as potential electrocatalysts for oxygen electrode reactions

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Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information R.rosetta: an interpretable machine learning framework.BMC Bioinformatics, 22(1):110, Mar 2021

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Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information A robust predictive diagnosis model for diabetes mellitus using shapley-incorporated machine learning algorithms.Healthcare Analytics, 3:100166, 2023

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Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Monty, Nicholas Hutchins, Moritz Linkmann, Ivan Marusic, and Ricardo Vinuesa

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Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Unresolved cited work

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Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Explainable machine learning for predicting homicide clearance in the united states.Journal of Criminal Justice, 79:101898, 2022

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Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Hysteresis response of groundwater depth on the influencing factors using an explainable learning model framework with shapley values.Science of The Total Environment, 904:166662,

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Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information doi: https://doi.org/10.1016/j.scitotenv.2023.166662

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Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Atmospheric water demand constrains net ecosystem production in subtropical mangrove forests.Journal of Hydrology, 630:130651, 2024

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Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Problems with Shapley-value-based explanations as feature importance measures

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Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Exploring pollutant joint effects in disease through interpretable machine learning.Journal of Hazardous Materials, 467:133707, 2024

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Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Manifold Restricted Interventional Shapley Values

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Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Unresolved cited work

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Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Machine-learning-assisted descriptors identification for indoor formaldehyde oxidation catalysts

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Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Food and Drug Administration

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Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Pima indians diabetes mellitus classification based on machine learning (ML) algorithms.Neural Comput Appl, pages 1–17, March 2022

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Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Feature relevance quantification in explainable AI: A causal problem

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Observation 33a9ed10-c098-407d-acaa-9e147038004a · outbound

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Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Vapnik.Statistical Learning Theory

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Resolution: unresolved; no resolver link, observed 2026-08-12T17:52:26.393234Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d5b9b8f2-1034-4120-8283-6d68ac580402 · outbound

This paper cites Contents of the code of practice on generative ai.

Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Contents of the code of practice on generative ai

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:52:28.498446Z

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 eb05376a-9730-45d5-bfd5-5d8440e749d8 · outbound

This paper cites Google LLC and Alphabet Inc v European Commission, 2024.

Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Google LLC and Alphabet Inc v European Commission, 2024

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:52:28.490703Z

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 a8a392b3-33ab-4bda-bd4a-b38bf2a9a429 · outbound

This paper cites Efficient fair pca for fair representation learning.

Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Efficient fair pca for fair representation learning

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-12T17:52:26.400990Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 659bb5bc-7b1a-4b82-b249-4ba9590c065c · outbound

This paper cites Food and Drug Administration.

Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Food and Drug Administration

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:52:28.475619Z

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 f3480530-58f7-493c-ae38-aeb13a3ee0f9 · outbound

This paper cites Sustainable ai regulation.Common Market Law Review, 61(2), 2024.

Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Sustainable ai regulation.Common Market Law Review, 61(2), 2024

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-12T17:52:26.406025Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0ab44c23-8325-4c83-8422-c93ac3a8d4e0 · outbound

This paper cites an unresolved cited work.

Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Unresolved cited work

Reference 64

Resolution: unresolved; raw_fallback, observed 2026-08-12T17:52:28.505677Z

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 67779e24-a930-45c4-929d-3eed65ebd6a4 · outbound

This paper cites URL https://ssrn.com/abstract=4924553.

Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information URL https://ssrn.com/abstract=4924553

Reference 65

Resolution: verified exact; doi, observed 2026-08-12T17:52:26.639345Z

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 4e5da072-ca0e-40b9-b223-f771098e7749 · outbound

This paper cites Springer Publishing Company, Incorporated, 1st edition, 2018.

Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Springer Publishing Company, Incorporated, 1st edition, 2018

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:52:28.444894Z

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 d481b33e-eb04-42e5-8bbf-1b279c9db341 · outbound

This paper cites A critical survey on fairness benefits of explainable ai.

Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information A critical survey on fairness benefits of explainable ai

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Resolution: unresolved; no resolver link, observed 2026-08-12T17:52:26.434740Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a04d0e45-4925-41d4-9cba-229335848f4f · outbound

This paper cites Google and Alphabet v.

Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Google and Alphabet v

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:52:28.483259Z

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 c10e43c8-fe96-4e80-b6c2-cedb3117e865 · outbound

This paper cites Sensitivity analysis in chemical kinetics.Annual Review of Physical Chemistry, 34(V olume 34, 1983):419–461, 1983.

Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Sensitivity analysis in chemical kinetics.Annual Review of Physical Chemistry, 34(V olume 34, 1983):419–461, 1983

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Resolution: metadata mismatch; raw_fallback, observed 2026-08-12T17:52:27.109453Z

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 99531e43-6c57-4fff-9cb1-865aca1187ab · outbound

This paper cites Food and Drug Administration.

Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Food and Drug Administration

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:52:28.468245Z

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 b19c77a1-b4dd-4f33-b4c5-975e64f2870d · outbound

This paper cites Reflection paper on the use of artifi- cial intelligence (AI) in the medicinal product lifecycle.

Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Reflection paper on the use of artifi- cial intelligence (AI) in the medicinal product lifecycle

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:52:28.460578Z

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 9a7c50c4-b904-4f15-8889-612f3f7611c0 · outbound

This paper cites Monitoring feature attributions: How google saved one of the largest ml services in trouble, September 29 2021.

Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Monitoring feature attributions: How google saved one of the largest ml services in trouble, September 29 2021

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:52:28.452803Z

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 9ea06f8d-c756-49ea-87e5-9814ec656862 · outbound

This paper cites Ablation Studies in Artificial Neural Networks.

Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Ablation Studies in Artificial Neural Networks

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-12T17:52:26.451124Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8c1208eb-b8ec-4f75-b203-90b768e00188 · outbound

This paper cites Pegourie, J.-M.

Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Pegourie, J.-M

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-12T17:52:26.455398Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 13abdd55-84cb-4c8a-84fb-fc9589a8a619 · outbound

This paper cites Sensitivity analysis for chemical models.Chemical Reviews, 105(7):2811–2828, 2005.

Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Sensitivity analysis for chemical models.Chemical Reviews, 105(7):2811–2828, 2005

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Resolution: verified exact; doi, observed 2026-08-12T17:52:26.631265Z

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 0b77b0b0-7a72-4ade-b26e-cd2dc76bb72c · outbound

This paper cites The gdnf protein familygene ablation studies reveal what they really do and how.Neu- ron, 22(2):201–203, 1999.

Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information The gdnf protein familygene ablation studies reveal what they really do and how.Neu- ron, 22(2):201–203, 1999

Reference 76

Resolution: verified exact; doi, observed 2026-08-12T17:52:26.602048Z

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 d24744a1-007b-461f-9cfc-fdfd59f2dfeb · outbound

This paper cites an unresolved cited work.

Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Unresolved cited work

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-12T17:52:26.443167Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 58cfa67a-4843-47b5-a34b-bdafe8c19627 · outbound

This paper cites Sensitivity analysis of spatial models.International Journal of Geographical Information Science, 23(2):151–168, 2009.

Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Sensitivity analysis of spatial models.International Journal of Geographical Information Science, 23(2):151–168, 2009

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Resolution: verified exact; doi, observed 2026-08-12T17:52:26.622577Z

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 c68e77d4-db77-416f-8215-713baf19f74a · outbound

This paper cites an unresolved cited work.

Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Unresolved cited work

Reference 79

Resolution: verified exact; doi, observed 2026-08-12T17:52:26.614422Z

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 fb9d5af8-5368-49f1-8c3f-9cc71d91323e · outbound

This paper cites Openxai: towards a transparent evaluation of post hoc model explanations.

Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Openxai: towards a transparent evaluation of post hoc model explanations

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:52:28.420391Z

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 025d73e3-7b30-4ef8-84f1-2d2a430e6a05 · outbound

This paper cites Why a right to explanation of automated decision-making does not exist in the general data protection regulation.International data privacy law, 7(2):76–99, 2017.

Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Why a right to explanation of automated decision-making does not exist in the general data protection regulation.International data privacy law, 7(2):76–99, 2017

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:52:28.411482Z

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 524ebc3d-6d51-406f-995f-47d940bae9e2 · outbound

This paper cites Boyd, Anthony Williams, and Richard Beyer.

Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Boyd, Anthony Williams, and Richard Beyer

Reference 82

Resolution: malformed identifier; no resolver link, observed 2026-08-12T17:52:26.458305Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 44e875ef-96e3-4140-8f80-7e61d51eb15c · outbound

This paper cites Address- ing the regulatory gap: moving towards an eu ai audit ecosystem beyond the ai act by including civil society.AI and Ethics, pages 1–22, 2024.

Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Address- ing the regulatory gap: moving towards an eu ai audit ecosystem beyond the ai act by including civil society.AI and Ethics, pages 1–22, 2024

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:52:28.395407Z

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 7082d887-e5bc-457b-9d48-e1ec618aa36d · outbound

This paper cites an unresolved cited work.

Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Unresolved cited work

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-12T17:52:26.464393Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d6a542ed-91d9-48e9-967b-ad051915ed4e · outbound

This paper cites Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (TCA V).

Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (TCA V)

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:52:28.436773Z

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 c2ddcdaf-4fa1-4441-bd5a-0bce2126927a · outbound

This paper cites Neural additive models: Interpretable machine learning with neural nets.Advances in neural information processing systems, 34:4699–4711, 2021.

Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Neural additive models: Interpretable machine learning with neural nets.Advances in neural information processing systems, 34:4699–4711, 2021

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:52:28.428837Z

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 445803f0-2e2c-40a9-8ef4-240ed06f15fc · outbound

This paper cites an unresolved cited work.

Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Unresolved cited work

Reference 87

Resolution: malformed identifier; raw_fallback, observed 2026-08-12T17:52:28.368654Z

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 4287f62f-4d7e-49f3-96c7-faf91c571b89 · outbound

This paper cites United States of America et al.

Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information United States of America et al

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:52:28.361628Z

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 24229c59-a33a-4e35-a97c-b48b15c0d8fb · outbound

This paper cites Ai regulation and the protection of source code.

Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Ai regulation and the protection of source code

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:52:28.403336Z

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 bffa446c-dee5-4e1d-a8e1-10356c9469a0 · outbound

This paper cites The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets.PLoS One, 10(3):e0118432, March 2015.

Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets.PLoS One, 10(3):e0118432, March 2015

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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 2d963bae-3f41-467d-858e-d6f592ae5d73 · outbound

This paper cites Black-box access is insufficient for rigorous ai audits.

Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Black-box access is insufficient for rigorous ai audits

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:52:28.387424Z

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 86dd0ad3-a716-419f-a039-7933f4ab25f8 · outbound

This paper cites Why fairness cannot be automated: Bridging the gap between eu non-discrimination law and ai.Computer Law & Security Review, 41:105567, 2021.

Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Why fairness cannot be automated: Bridging the gap between eu non-discrimination law and ai.Computer Law & Security Review, 41:105567, 2021

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Resolution: unresolved; no resolver link, observed 2026-08-12T17:52:26.485429Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3cc9a078-89a6-4050-bc92-3b3fb029f3c4 · outbound

This paper cites The theory of artificial immutability: Protecting algorithmic groups under anti- discrimination law.Tul.

Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information The theory of artificial immutability: Protecting algorithmic groups under anti- discrimination law.Tul

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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 21ff32f9-ee62-4fc7-b92f-a07114aa56e3 · outbound

This paper cites Statlog (German Credit Data).

Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Statlog (German Credit Data)

Reference 94

Resolution: unresolved; no resolver link, observed 2026-08-12T17:52:26.511811Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c9b018d7-077e-4d74-b816-2dd4da2a0534 · outbound

This paper cites yuzie007/mpltern: 1.0.4.

Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information yuzie007/mpltern: 1.0.4

Reference 95

Resolution: malformed identifier; no resolver link, observed 2026-08-12T17:52:26.514411Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation da6f6b58-ccf5-4ba0-aca0-e9a4ac75026e · outbound

This paper cites Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference.

Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference

Reference 96

Resolution: unresolved; no resolver link, observed 2026-08-12T17:52:26.497413Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation feb55229-db9c-44ab-aae2-4667a83904a5 · outbound

This paper cites BERT: Pre-training of deep bidirectional transformers for language understanding.

Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information BERT: Pre-training of deep bidirectional transformers for language understanding

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Resolution: malformed identifier; no resolver link, observed 2026-08-12T17:52:26.503100Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5086fe52-f152-46ff-9c45-73c17ed6d67b · outbound

This paper cites How we analyzed the com- pas recidivism algorithm, May 2016.

Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information How we analyzed the com- pas recidivism algorithm, May 2016

Reference 99

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:52:28.346311Z

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 78b35738-484c-48ad-bb7f-20a7b61975ec · outbound

This paper cites Communities and Crime.

Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Communities and Crime

Reference 100

Resolution: unresolved; no resolver link, observed 2026-08-12T17:52:26.509227Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ebca3d95-6517-4176-901e-b545da33da9c · outbound

This paper cites an unresolved cited work.

Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Unresolved cited work

Reference 103

Resolution: unresolved; raw_fallback, observed 2026-08-12T17:52:28.338797Z

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 afc459d7-384a-4a1d-bb37-f7f869345287 · outbound

This paper cites an unresolved cited work.

Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information Unresolved cited work

Reference 105

Resolution: unresolved; raw_fallback, observed 2026-08-12T17:52:28.331415Z

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.

Coverage vector

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Outbound references

Pith citing papers