The DOI matches its Crossref record; 23 of the 24 references it cites were found in a registry or an index.
Explainable AI: The New 42?
Open accessOverview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“I show how archival theories have been animated in documenting data in the context of AI and ML, including through documentation frameworks like Datasheets for Datasets”
From Can AI provenance inform archival provenance? (2026) · cites this paper as Goebel et al. 2018
Citations
Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature · doi:10.1038/nature14236 · Cited by 30,357
Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939778 · Cited by 17,555
Dermatologist-level classification of skin cancer with deep neural networks
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017) · Nature · doi:10.1038/nature21056 · Cited by 14,362
DeepFace: Closing the Gap to Human-Level Performance in Face Verification
Taigman, Y., Yang, M., Ranzato, M., & Wolf, L. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.220 · Cited by 6,542
DeepStack: Expert-level artificial intelligence in heads-up no-limit poker
Moravčík, M., Schmid, M., BURCH, N. R., Lisý, V., Morrill, D., Bard, N., Davis, T., Waugh, K., Johanson, M., & Bowling, M. (2017) · Science · doi:10.1126/science.aam6960 · Cited by 885
What do we need to build explainable AI systems for the medical domain?
Holzinger, A., Biemann, C., Pattichis, C. S., & Kell, D. B. (2017) · arXiv · doi:10.48550/arxiv.1712.09923 · Cited by 636
Self-citationPrinciples of Explanatory Debugging to Personalize Interactive Machine Learning
Kulesza, T., Burnett, M., Wong, W.-K., & Stumpf, S. (2015) · Proceedings of the 20th International Conference on Intelligent User Interfaces · doi:10.1145/2678025.2701399 · Cited by 576
Self-citationGenerating Visual Explanations
Hendricks, L. A., Akata, Z., Rohrbach, M., Donahue, J., Schiele, B., & Darrell, T. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46493-0_1 · Cited by 534
Self-citationMultimodal Explanations: Justifying Decisions and Pointing to the Evidence
Park, D. H., Hendricks, L. A., Akata, Z., Rohrbach, A., Schiele, B., Darrell, T., & Rohrbach, M. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00915 · Cited by 418
Self-citationHuman Activity Recognition Using Recurrent Neural Networks
Singh, D., Merdivan, E., Psychoula, I., Kropf, J., Hanke, S., Geist, M., & Holzinger, A. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-66808-6_18 · Cited by 195
Self-citationHolzinger, A., Plass, M., Holzinger, K., Crişan, G. C., Pintea, C.-M., & Palade, V. (2019) · Creative Mathematics and Informatics · doi:10.37193/cmi.2019.02.04 · Cited by 111
Self-citationHolzinger, A., Plass, M., Holzinger, K., Crişan, G. C., Pintea, C.-M., & Palade, V. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-45507-5_6 · Cited by 98
Self-citationTowards the Augmented Pathologist: Challenges of Explainable-AI in Digital PathologyPreprint
Holzinger, A., Malle, B., Kieseberg, P., Roth, P. M., Müller, H., Reihs, R., & Zatloukal, K. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1712.06657 · Cited by 93
Self-citationMachine Learning Enhanced Virtual Autopsy
Sullivan, S. O., Holzinger, A., Zatloukal, K., Saldiva, P. H. N., Sajid, M. I., & Wichmann, D. (2017) · Autopsy and Case Reports · doi:10.4322/acr.2017.037 · Cited by 30
Self-citationWorking with Beliefs: AI Transparency in the Enterprise.
Chander, A., Srinivasan, R., Chelian, S. E., Wang, J., & Uchino, K. (2018) · IUI Workshops · Cited by 27
Self-citationCan we Trust Machine Learning Results? Artificial Intelligence in Safety-Critical Decision Support.
Holzinger, K., Mak, K., Kieseberg, P., & Holzinger, A. (2018) · ERCIM news/ERCIM news online edition · Cited by 27
Self-citationLearning Multi-Way Relations via Tensor Decomposition With Neural Networks
Maruhashi, K., Todoriki, M., Ohwa, T., Goto, K., Hasegawa, Y., Inakoshi, H., & Anai, H. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11760 · Cited by 24
An Introduction to Deep Visual ExplanationPreprint
Babiker, H. K. B., & Goebel, R. G. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.09482 · Cited by 16
Self-citationSemantic Explanations of PredictionsPreprint
Lécué, F., & Wu, J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.10587 · Cited by 13
Evaluating Explanations by Cognitive Value
Chander, A., & Srinivasan, R. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-99740-7_23 · Cited by 13
Self-citationUsing KL-divergence to focus Deep Visual ExplanationPreprint
Babiker, H. K. B., & Goebel, R. G. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.06431 · Cited by 9
Self-citation
Research works printed but not linked: 1
- Chen, J., Lecue, F., Pan, J.Z., Horrocks, I., Chen, H.: Transfer learning explanation with ontologies. In: Principles of Knowledge Representation and Reasoning: Proceedings of the Eleventh International Conference, KR 2018, 30 October–2 November 2018, Tempe, Arizona (USA) (2018, to appear)
Cited by
Sorted by citations
Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., et al. (2020) · Information Fusion · doi:10.1016/j.inffus.2019.12.012 · Cited by 10,423
Causability and explainability of artificial intelligence in medicine
Holzinger, A., Langs, G., Denk, H., Zatloukal, K., & Müller, H. (2019) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1312 · Cited by 1,885
Self-citationEvaluating the quality of machine learning explanations: a survey on methods and metrics
Zhou, J., Gandomi, A. H., Chen, F., & Holzinger, A. (2021) · Electronics · doi:10.3390/electronics10050593 · Cited by 606
Self-citationFairness of artificial intelligence in healthcare: review and recommendations
Ueda, D., Kakinuma, T., Fujita, S., Kamagata, K., Fushimi, Y., Ito, R., Matsui, Y., Nozaki, T., Nakaura, T., Fujima, N., et al. (2024) · Japanese Journal of Radiology · doi:10.1007/s11604-023-01474-3 · Cited by 607
Notions of explainability and evaluation approaches for explainable artificial intelligence
Vilone, G., & Longo, L. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.05.009 · Cited by 598
Metadata
- Authors
- Goebel, R., Chander, A., Holzinger, K., Lecue, F., Akata, Z., Stumpf, S., Kieseberg, P., & Holzinger, A.
- Affiliations
- University of Alberta (CA)
- Fujitsu (United States) (US)
- SBA Research (AT)
- Max Planck Institute for Informatics (DE)
- City, University of London (GB)
- University of Applied Sciences St Pölten (AT)
- Medical University of Graz (AT)
- Corresponding author
- Goebel, Randy
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2018
- Volume
- LNCS-11015
- Pages
- 295-303
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2888580379
- Cited by
- 259
- References
- 24
- Citations by year
- 2026: 11 · 2025: 11 · 2024: 27 · 2023: 37 · 2022: 45 · 2021: 49 · 2020: 47 · 2019: 29 · 2018: 3
- FWCI
- 12.90
- Type
- conference paper
- Language
- en
- Open access
- green
- inria.hal.science
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- City Research Online (City University London) (acceptedVersion)
- HAL (submittedVersion, cc-by)
- ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam) (submittedVersion, cc-by-sa)
- Max Planck Digital Library (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Artificial Intelligence › Logic, Reasoning, and Knowledge
- Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
- Keywords
- explainable AI, explainability, artificial neural networks, black-box models, expert systems, abductive reasoning, probabilistic reasoning, uncertainty management, interpretability, contextual adaptation, human-AI augmentation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
