Fundamental Limitation in Explaining AI | Paper Citation Record

Observation 55ecdf75-fa65-4e67-a0fb-e0c7660deb75 · outbound

This paper cites Language models are unsupervised multitask learners.

Fundamental Limitation in Explaining AI Language models are unsupervised multitask learners

Reference 1

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Observation d04050f0-c16a-4bf9-8b9c-787331e33bae · outbound

This paper cites High-resolution image syn- thesis with latent diffusion models,.

Fundamental Limitation in Explaining AI High-resolution image syn- thesis with latent diffusion models,

Reference 2

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Observation 327dc89f-78d9-47a3-8db9-84a819038af1 · outbound

This paper cites Artiﬁcial intelligence risk management frame- work: Generative artiﬁcial intelligence proﬁle,.

Fundamental Limitation in Explaining AI Artiﬁcial intelligence risk management frame- work: Generative artiﬁcial intelligence proﬁle,

Reference 3

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This paper cites Guidance for risk management of artiﬁcial intelligence systems,.

Fundamental Limitation in Explaining AI Guidance for risk management of artiﬁcial intelligence systems,

Reference 4

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This paper cites A uniﬁed approach to interpreting model predictions,.

Fundamental Limitation in Explaining AI A uniﬁed approach to interpreting model predictions,

Reference 5

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Observation 025ba90d-7fe1-4131-9322-d331138bdc6e · outbound

This paper cites From local explanations to global understanding with explainable ai for trees,.

Fundamental Limitation in Explaining AI From local explanations to global understanding with explainable ai for trees,

Reference 6

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Observation 4caf06ee-9680-4c8d-bbdc-896f22f23d7d · outbound

This paper cites Axiomatic attribution for deep networks,.

Fundamental Limitation in Explaining AI Axiomatic attribution for deep networks,

Reference 7

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Observation cc17b13a-8b3c-4c9f-a9bb-38d78c3d6156 · outbound

This paper cites Explaining explanations: Axiomatic feature interac- tions for deep networks,.

Fundamental Limitation in Explaining AI Explaining explanations: Axiomatic feature interac- tions for deep networks,

Reference 8

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Observation 3d703d74-327e-4ae6-b6b7-7afd137251f4 · outbound

This paper cites NormLime: A New Feature Importance Metric for Explaining Deep Neural Networks.

Fundamental Limitation in Explaining AI NormLime: A New Feature Importance Metric for Explaining Deep Neural Networks

Reference 9

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Observation ff9626b9-3b94-4bd5-82a1-b9e6bededbb8 · outbound

This paper cites Grad-cam: Visual explanations from deep networks via gradient-based localization,.

Fundamental Limitation in Explaining AI Grad-cam: Visual explanations from deep networks via gradient-based localization,

Reference 10

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Observation 8530486b-c044-48a5-a651-c6145452017e · outbound

This paper cites Grad-cam++: General- ized gradient-based visual explanations for deep convolutional networks,.

Fundamental Limitation in Explaining AI Grad-cam++: General- ized gradient-based visual explanations for deep convolutional networks,

Reference 11

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Observation 809f58c6-db60-4ff7-829d-a2d5a1a066d6 · outbound

This paper cites Score- cam: Score-weighted visual explanations for convolutional neural networks,.

Fundamental Limitation in Explaining AI Score- cam: Score-weighted visual explanations for convolutional neural networks,

Reference 12

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Observation 611c8795-100b-4c2d-a502-ed7e7648198f · outbound

This paper cites Axiom-based Grad-CAM: Towards Accurate Visualization and Explanation of CNNs.

Fundamental Limitation in Explaining AI Axiom-based Grad-CAM: Towards Accurate Visualization and Explanation of CNNs

Reference 13

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Observation aa626692-d4b5-4838-9a78-195aeb9928ef · outbound

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

Fundamental Limitation in Explaining AI Counterfactual explanations without opening the black box: Automated decisions and the gdpr

Reference 14

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Observation 5019beb1-7dcd-4d13-9b74-b65466196381 · outbound

This paper cites Algorithmic recourse: from counterfactual explana- tions to interventions,.

Fundamental Limitation in Explaining AI Algorithmic recourse: from counterfactual explana- tions to interventions,

Reference 15

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Observation 50aecb90-ef43-4eaa-853b-c50e3f028c7c · outbound

This paper cites Can Large Language Models Explain Themselves? A Study of LLM-Generated Self-Explanations.

Fundamental Limitation in Explaining AI Can Large Language Models Explain Themselves? A Study of LLM-Generated Self-Explanations

Reference 16

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Observation 5238801a-0b47-4872-8b29-f5ab5ccba92b · outbound

This paper cites In-Context Explainers: Harnessing LLMs for Explaining Black Box Models.

Fundamental Limitation in Explaining AI In-Context Explainers: Harnessing LLMs for Explaining Black Box Models

Reference 17

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Observation bda0972b-bcb3-405e-a2f6-3b22f6e49fc8 · outbound

This paper cites How interpretable are reasoning explanations from prompting large language models?,.

Fundamental Limitation in Explaining AI How interpretable are reasoning explanations from prompting large language models?,

Reference 18

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Observation 93ae632a-3c5c-482b-8af3-7698d30da79f · outbound

This paper cites " why should i trust you?.

Fundamental Limitation in Explaining AI " why should i trust you?

Reference 19

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Observation 43e0c423-de28-4719-b911-849b63754e73 · outbound

This paper cites Anchors: High-precision model-agnostic explana- tions,.

Fundamental Limitation in Explaining AI Anchors: High-precision model-agnostic explana- tions,

Reference 20

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Observation 85b6cd24-ec87-4d50-afc1-e123384b25ff · outbound

This paper cites Glocalx-from local to global explanations of black box ai models,.

Fundamental Limitation in Explaining AI Glocalx-from local to global explanations of black box ai models,

Reference 21

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Observation bb40c6ca-1b39-4385-87a6-a53f43fd7469 · outbound

This paper cites GLEAMS: Bridging the Gap Between Local and Global Explanations.

Fundamental Limitation in Explaining AI GLEAMS: Bridging the Gap Between Local and Global Explanations

Reference 22

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Observation 3771ce01-11e8-4b76-92c9-6ea5bae506c8 · outbound

This paper cites Extracting tree-structured representations of trained networks,.

Fundamental Limitation in Explaining AI Extracting tree-structured representations of trained networks,

Reference 23

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Observation 025d4ac8-ac7d-4ee0-b7c2-fe01cd071705 · outbound

This paper cites Understanding neural networks via rule extraction,.

Fundamental Limitation in Explaining AI Understanding neural networks via rule extraction,

Reference 24

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Observation 2cb9bd7d-9b54-46f1-bec7-665be117ec40 · outbound

This paper cites Ai/ml for network security: The emperor has no clothes,.

Fundamental Limitation in Explaining AI Ai/ml for network security: The emperor has no clothes,

Reference 25

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Observation 89d05908-4248-45aa-ad58-993dc17b8fb9 · outbound

This paper cites Expected Grad-CAM: Towards gradient faithfulness.

Fundamental Limitation in Explaining AI Expected Grad-CAM: Towards gradient faithfulness

Reference 26

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Observation dccbd173-ee19-48bd-b88a-417952c5114b · outbound

This paper cites Language models don’t always say what they think: Unfaithful explanations in chain-of-thought prompting,.

Fundamental Limitation in Explaining AI Language models don’t always say what they think: Unfaithful explanations in chain-of-thought prompting,

Reference 27

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Observation f9049ec0-22a6-4eca-be5e-35b280d9be60 · outbound

This paper cites Explain- ability for large language models: A survey,.

Fundamental Limitation in Explaining AI Explain- ability for large language models: A survey,

Reference 28

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Observation 99c5728c-45db-46bd-b9c3-0cca92cf4582 · outbound

This paper cites Explainable artiﬁcial intelligence (xai): Con- cepts, taxonomies, opportunities and challenges toward responsible ai,.

Fundamental Limitation in Explaining AI Explainable artiﬁcial intelligence (xai): Con- cepts, taxonomies, opportunities and challenges toward responsible ai,

Reference 29

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Observation 9ef452d4-97e3-432d-a7bb-d6faf989add5 · outbound

This paper cites Explainable generative ai: A two-stage review of existing techniques and future research directions,.

Fundamental Limitation in Explaining AI Explainable generative ai: A two-stage review of existing techniques and future research directions,

Reference 30

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Observation 293e625b-d3af-4ce4-9b6e-fd899e6380e9 · outbound

This paper cites How many words do we read per minute? a review and meta-analysis of reading rate,.

Fundamental Limitation in Explaining AI How many words do we read per minute? a review and meta-analysis of reading rate,

Reference 31

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Observation e7bf5dad-b6a4-4994-804d-b1b9073a8cf5 · outbound

This paper cites Most decimal places of pi memorized,.

Fundamental Limitation in Explaining AI Most decimal places of pi memorized,

Reference 32

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Observation f9106501-4f0e-44bb-a097-fe8ea2d37531 · outbound

This paper cites Trade-off between efﬁciency and consistency for removal-based explanations,.

Fundamental Limitation in Explaining AI Trade-off between efﬁciency and consistency for removal-based explanations,

Reference 33

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Observation f65b4779-5470-467d-ab79-995712906cf9 · outbound

This paper cites Impossibility theorems for feature attribution,.

Fundamental Limitation in Explaining AI Impossibility theorems for feature attribution,

Reference 34

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Observation c8628d25-a6b9-4bcb-8958-4712dd4ceef9 · outbound

This paper cites A the- ory of interpretable approximations,.

Fundamental Limitation in Explaining AI A the- ory of interpretable approximations,

Reference 35

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Observation fd090a16-40d5-479f-a106-042e5f6cf689 · outbound

This paper cites Partially interpretable models with guarantees on coverage and accuracy,.

Fundamental Limitation in Explaining AI Partially interpretable models with guarantees on coverage and accuracy,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T03:15:57.835889Z

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Observation 499f601b-783e-4e8f-893b-90f89cd22104 · outbound

This paper cites Kolmogorov complexity bounds for llm steganography and a perplexity-based detection proxy,.

Fundamental Limitation in Explaining AI Kolmogorov complexity bounds for llm steganography and a perplexity-based detection proxy,

Reference 37

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Observation 59fde7fd-8119-461b-9580-f581387b38f0 · outbound

This paper cites Conversational complexity for assessing risk in large language models,.

Fundamental Limitation in Explaining AI Conversational complexity for assessing risk in large language models,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T03:15:57.890983Z

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Observation d693b906-6701-47cd-9627-aa642a3cbe29 · outbound

This paper cites Understanding llm behaviors via compression: Data gener- ation, knowledge acquisition and scaling laws,.

Fundamental Limitation in Explaining AI Understanding llm behaviors via compression: Data gener- ation, knowledge acquisition and scaling laws,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T03:15:57.830623Z

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Observation 86045db0-7500-4205-b815-376669b2c200 · outbound

This paper cites Language modeling is compression,.

Fundamental Limitation in Explaining AI Language modeling is compression,

Reference 40

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Observation 96bf2c9b-e8c5-4d23-b112-a351784b66b1 · outbound

This paper cites In-context learning and occam’s razor,.

Fundamental Limitation in Explaining AI In-context learning and occam’s razor,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T03:15:57.832446Z

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Observation 88a8bb86-a14e-404f-9fac-fe56c42eb57a · outbound

This paper cites A neural probabilistic language model,.

Fundamental Limitation in Explaining AI A neural probabilistic language model,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T03:15:57.837685Z

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Observation 25265302-35a1-4d88-9e70-7b735fec9198 · outbound

This paper cites Recurrent neural network based language model.,.

Fundamental Limitation in Explaining AI Recurrent neural network based language model.,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T03:15:57.841138Z

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Observation dd56e9df-72e9-4825-a05a-173f97106462 · outbound

This paper cites A formal theory of inductive inference. part i,.

Fundamental Limitation in Explaining AI A formal theory of inductive inference. part i,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T03:15:57.819343Z

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Observation 9c746325-837f-4eb8-a25e-160a46b7e735 · outbound

This paper cites A formal theory of inductive inference. part ii,.

Fundamental Limitation in Explaining AI A formal theory of inductive inference. part ii,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T03:15:57.817495Z

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Observation 17d90c08-a5ef-4dcd-871d-573b3a74f714 · outbound

This paper cites Three approaches to the quantitative deﬁnition of information.

Fundamental Limitation in Explaining AI Three approaches to the quantitative deﬁnition of information

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T03:15:57.821342Z

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Observation b4f778e1-fad5-4792-ad6a-73bdde3d6b39 · outbound

This paper cites On the simplicity and speed of programs for computing inﬁnite sets of natural numbers,.

Fundamental Limitation in Explaining AI On the simplicity and speed of programs for computing inﬁnite sets of natural numbers,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T03:15:57.825018Z

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Observation 189ccead-d2e9-41e8-b4f2-c06b1ac33bbf · outbound

This paper cites Attention is all you need,.

Fundamental Limitation in Explaining AI Attention is all you need,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T03:15:57.812226Z

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Observation 12a48753-ab90-4eb3-af77-e51d68bfe708 · outbound

This paper cites Cutland, Computability: An introduction to recursive function theory.

Fundamental Limitation in Explaining AI Cutland, Computability: An introduction to recursive function theory

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T03:15:57.813988Z

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Observation 3ab30c07-7e44-499d-b935-411f8ae87b6b · outbound

This paper cites an unresolved cited work.

Fundamental Limitation in Explaining AI Unresolved cited work

Reference 50

Resolution: unresolved; raw_fallback, observed 2026-07-09T03:15:57.815629Z

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Observation f8deccbd-92cc-4744-a071-9d1fea68df91 · outbound

This paper cites Algorithmic information theory,.

Fundamental Limitation in Explaining AI Algorithmic information theory,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T03:15:57.823158Z

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Observation 0e9a1830-4b04-4364-b52d-0eb687d92047 · outbound

This paper cites Together with x0 · x1 = x′ 0 · x′ 1, this also implies x1 = x′.

Fundamental Limitation in Explaining AI Together with x0 · x1 = x′ 0 · x′ 1, this also implies x1 = x′

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T03:15:57.826946Z

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Observation 83d9f374-f575-4cee-a558-8aa6f7b25652 · outbound

This paper cites For a general n-variable pairing, since ⟨x0, x1, ..., xn−2, xn−1⟩ = ⟨x0, ⟨x1, ..., ⟨xn−2, xn−1⟩ · · · ⟩⟩, injectivity is easily shown by mathematical induction.

Fundamental Limitation in Explaining AI For a general n-variable pairing, since ⟨x0, x1, ..., xn−2, xn−1⟩ = ⟨x0, ⟨x1, ..., ⟨xn−2, xn−1⟩ · · · ⟩⟩, injectivity is easily shown by mathematical induction

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T03:15:57.844953Z

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