Поясненний штучний інтелект (Ukrainian Wikipedia)

Clancey, William (1987). Knowledge-Based Tutoring: The GUIDON Program. Cambridge, Massachusetts: The MIT Press.
Brown, John S.; Burton, R. R.; De Kleer, Johan (1982). Pedagogical, natural language, and knowledge engineering techniques in SOPHIE I, II, and III. Intelligent Tutoring Systems. Academic Press. ISBN 0-12-648680-8.
Russell, Stuart; Norvig, Peter (2003). Artificial Intelligence: A Modern Approach. Prentice Hall Series in Artificial Intelligence (вид. Second). Upper Saddle River, New Jersey: Prentice Hall, Pearson Education. ISBN 0-13-790395-2.

arxiv.org

Roscher, R.; Bohn, B.; Duarte, M. F.; Garcke, J. (2020). Explainable Machine Learning for Scientific Insights and Discoveries. IEEE Access. 8: 42200—42216. arXiv:1905.08883. doi:10.1109/ACCESS.2020.2976199. ISSN 2169-3536.
Murdoch, W. James; Singh, Chandan; Kumbier, Karl; Abbasi-Asl, Reza; Yu, Bin (14 січня 2019). Interpretable machine learning: definitions, methods, and applications. Proceedings of the National Academy of Sciences of the United States of America. 116 (44): 22071—22080. arXiv:1901.04592. Bibcode:2019arXiv190104592M. doi:10.1073/pnas.1900654116. PMC 6825274. PMID 31619572.
Rudin, Cynthia (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence (англ.). 1 (5): 206—215. arXiv:1811.10154. doi:10.1038/s42256-019-0048-x. ISSN 2522-5839.
Biecek, Przemyslaw (23 червня 2018). DALEX: explainers for complex predictive models. Journal of Machine Learning Research. 19: 1—5. arXiv:1806.08915. Bibcode:2018arXiv180608915B.
Csiszár, Orsolya; Csiszár, Gábor; Dombi, József (8 липня 2020). Interpretable neural networks based on continuous-valued logic and multicriteria decision operators. Knowledge-Based Systems (англ.). 199: 105972. arXiv:1910.02486. doi:10.1016/j.knosys.2020.105972. ISSN 0950-7051.
Murdoch, W. James; Singh, Chandan; Kumbier, Karl; Abbasi-Asl, Reza; Yu, Bin (14 січня 2019). Interpretable machine learning: definitions, methods, and applications. Proceedings of the National Academy of Sciences of the United States of America. 116 (44): 22071—22080. arXiv:1901.04592. Bibcode:2019arXiv190104592M. doi:10.1073/pnas.1900654116. PMC 6825274. PMID 31619572.
Lundberg, Scott M; Lee, Su-In (2017), Guyon, I.; Luxburg, U. V.; Bengio, S.; Wallach, H. (ред.), A Unified Approach to Interpreting Model Predictions (PDF), Advances in Neural Information Processing Systems 30, Curran Associates, Inc.: 4765—4774, arXiv:1705.07874, Bibcode:2017arXiv170507874L, процитовано 13 березня 2020
Aivodji, Ulrich; Arai, Hiromi; Fortineau, Olivier; Gambs, Sébastien; Hara, Satoshi; Tapp, Alain (24 травня 2019). Fairwashing: the risk of rationalization. International Conference on Machine Learning (англ.). PMLR: 161—170. arXiv:1901.09749.
Le Merrer, Erwan; Trédan, Gilles (September 2020). Remote explainability faces the bouncer problem. Nature Machine Intelligence (англ.). 2 (9): 529—539. arXiv:1910.01432. doi:10.1038/s42256-020-0216-z. ISSN 2522-5839.
Rudin, Cynthia (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence (англ.). 1 (5): 206—215. arXiv:1811.10154. doi:10.1038/s42256-019-0048-x. ISSN 2522-5839.
Vidal, Thibaut; Schiffer, Maximilian (2020). Born-Again Tree Ensembles. International Conference on Machine Learning (англ.). PMLR. 119: 9743—9753. arXiv:2003.11132.
Hendricks, Lisa Anne; Akata, Zeynep; Rohrbach, Marcus; Donahue, Jeff; Schiele, Bernt; Darrell, Trevor (2016). Generating Visual Explanations. Computer Vision – ECCV 2016. Lecture Notes in Computer Science (англ.). Springer International Publishing. 9908: 3—19. arXiv:1603.08507. doi:10.1007/978-3-319-46493-0_1. ISBN 978-3-319-46492-3.
Koh, Pang Wei; Liang, Percy (17 липня 2017). Understanding Black-box Predictions via Influence Functions. International Conference on Machine Learning (англ.). PMLR: 1885—1894. arXiv:1703.04730.
Qureshi, M. Atif; Greene, Derek (4 червня 2018). EVE: explainable vector based embedding technique using Wikipedia. Journal of Intelligent Information Systems (англ.). 53: 137—165. arXiv:1702.06891. doi:10.1007/s10844-018-0511-x. ISSN 0925-9902.
Galhotra, Sainyam; Pradhan, Romila; Salimi, Babak (2021). Explaining black-box algorithms using probabilistic contrastive counterfactuals}. ACM Sigmod. 2021: 577—590. arXiv:2103.11972. doi:10.1145/3448016.3458455. ISBN 9781450383431.

bloomberg.com

DeepMind Has Simple Tests That Might Prevent Elon Musk's AI Apocalypse. Bloomberg.com (англ.). 11 грудня 2017. Процитовано 30 січня 2018.
Accenture Unveils Tool to Help Companies Insure Their AI Is Fair. Bloomberg.com (англ.). June 2018. Процитовано 5 серпня 2018.
Kahn, Jeremy (12 грудня 2018). Artificial Intelligence Has Some Explaining to Do. Bloomberg Businessweek. Процитовано 17 грудня 2018.

cftc.gov

The Flash Crash: The Impact of High Frequency Trading on an Electronic Market (PDF). CFTC. CFTC. Процитовано 17 липня 2017.

city.ac.uk

openaccess.city.ac.uk

Gunning, D.; Stefik, M.; Choi, J.; Miller, T.; Stumpf, S.; Yang, G.-Z. (18 грудня 2019). XAI-Explainable artificial intelligence. Science Robotics (англ.). 4 (37): eaay7120. doi:10.1126/scirobotics.aay7120. ISSN 2470-9476. PMID 33137719.

darpa.mil

Explainable Artificial Intelligence (XAI). DARPA. DARPA. Процитовано 17 липня 2017.

deepai.org

Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI. DeepAI. 22 жовтня 2019. Процитовано 13 січня 2021.

distill.pub

Olah, Chris; Cammarata, Nick; Schubert, Ludwig; Goh, Gabriel; Petrov, Michael; Carter, Shan (10 березня 2020). Zoom In: An Introduction to Circuits. Distill (англ.). 5 (3): e00024.001. doi:10.23915/distill.00024.001. ISSN 2476-0757.

doi.org

Janssen, Femke M.; Aben, Katja K. H.; Heesterman, Berdine L.; Voorham, Quirinus J. M.; Seegers, Paul A.; Moncada-Torres, Arturo (February 2022). Using Explainable Machine Learning to Explore the Impact of Synoptic Reporting on Prostate Cancer. Algorithms (англ.). 15 (2): 49. doi:10.3390/a15020049. ISSN 1999-4893.
Alizadeh, Fatemeh (2021). I Don't Know, Is AI Also Used in Airbags?: An Empirical Study of Folk Concepts and People's Expectations of Current and Future Artificial Intelligence. Icom. doi:10.1515/icom-2021-0009. S2CID 233328352.
Gunning, D.; Stefik, M.; Choi, J.; Miller, T.; Stumpf, S.; Yang, G.-Z. (18 грудня 2019). XAI-Explainable artificial intelligence. Science Robotics (англ.). 4 (37): eaay7120. doi:10.1126/scirobotics.aay7120. ISSN 2470-9476. PMID 33137719.
Rieg, Thilo; Frick, Janek; Baumgartl, Hermann; Buettner, Ricardo (17 грудня 2020). Demonstration of the potential of white-box machine learning approaches to gain insights from cardiovascular disease electrocardiograms. PLOS ONE (англ.). 15 (12): e0243615. Bibcode:2020PLoSO..1543615R. doi:10.1371/journal.pone.0243615. ISSN 1932-6203. PMC 7746264. PMID 33332440.
Loyola-González, O. (2019). Black-Box vs. White-Box: Understanding Their Advantages and Weaknesses From a Practical Point of View. IEEE Access. 7: 154096—154113. doi:10.1109/ACCESS.2019.2949286. ISSN 2169-3536.
Roscher, R.; Bohn, B.; Duarte, M. F.; Garcke, J. (2020). Explainable Machine Learning for Scientific Insights and Discoveries. IEEE Access. 8: 42200—42216. arXiv:1905.08883. doi:10.1109/ACCESS.2020.2976199. ISSN 2169-3536.
Murdoch, W. James; Singh, Chandan; Kumbier, Karl; Abbasi-Asl, Reza; Yu, Bin (14 січня 2019). Interpretable machine learning: definitions, methods, and applications. Proceedings of the National Academy of Sciences of the United States of America. 116 (44): 22071—22080. arXiv:1901.04592. Bibcode:2019arXiv190104592M. doi:10.1073/pnas.1900654116. PMC 6825274. PMID 31619572.
Lipton, Zachary C. (June 2018). The Mythos of Model Interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue (англ.). 16 (3): 31—57. doi:10.1145/3236386.3241340. ISSN 1542-7730.
Montavon, Grégoire; Samek, Wojciech; Müller, Klaus-Robert (1 лютого 2018). Methods for interpreting and understanding deep neural networks. Digital Signal Processing (англ.). 73: 1—15. doi:10.1016/j.dsp.2017.10.011. ISSN 1051-2004.
Adadi, A.; Berrada, M. (2018). Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI). IEEE Access. 6: 52138—52160. doi:10.1109/ACCESS.2018.2870052. ISSN 2169-3536.
Rudin, Cynthia (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence (англ.). 1 (5): 206—215. arXiv:1811.10154. doi:10.1038/s42256-019-0048-x. ISSN 2522-5839.
Dosilovic, Filip; Brcic, Mario; Hlupic, Nikica (25 травня 2018). Explainable Artificial Intelligence: A Survey (PDF). MIPRO 2018 - 41st International Convention Proceedings. MIPRO 2018. Opatija, Croatia. с. 210—215. doi:10.23919/MIPRO.2018.8400040. ISBN 978-953-233-095-3. Архів оригіналу (PDF) за 10 грудня 2018. Процитовано 9 грудня 2018.
Aler Tubella, Andrea; Theodorou, Andreas; Dignum, Frank; Dignum, Virginia (2019). Governance by Glass-Box: Implementing Transparent Moral Bounds for AI Behaviour. Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization. с. 5787—5793. doi:10.24963/ijcai.2019/802. ISBN 978-0-9992411-4-1.
Sokol, Kacper; Flach, Peter (2018). Glass-Box: Explaining AI Decisions With Counterfactual Statements Through Conversation With a Voice-enabled Virtual Assistant. Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence. с. 5868—5870. doi:10.24963/ijcai.2018/865. ISBN 9780999241127.
Tickle, A. B.; Andrews, R.; Golea, M.; Diederich, J. (November 1998). The truth will come to light: directions and challenges in extracting the knowledge embedded within trained artificial neural networks. IEEE Transactions on Neural Networks. 9 (6): 1057—1068. doi:10.1109/72.728352. ISSN 1045-9227. PMID 18255792.
Csiszár, Orsolya; Csiszár, Gábor; Dombi, József (8 липня 2020). Interpretable neural networks based on continuous-valued logic and multicriteria decision operators. Knowledge-Based Systems (англ.). 199: 105972. arXiv:1910.02486. doi:10.1016/j.knosys.2020.105972. ISSN 0950-7051.
Murdoch, W. James; Singh, Chandan; Kumbier, Karl; Abbasi-Asl, Reza; Yu, Bin (14 січня 2019). Interpretable machine learning: definitions, methods, and applications. Proceedings of the National Academy of Sciences of the United States of America. 116 (44): 22071—22080. arXiv:1901.04592. Bibcode:2019arXiv190104592M. doi:10.1073/pnas.1900654116. PMC 6825274. PMID 31619572.
Dombi, József; Csiszár, Orsolya (2021). Explainable Neural Networks Based on Fuzzy Logic and Multi-criteria Decision Tools. Studies in Fuzziness and Soft Computing (брит.). 408. doi:10.1007/978-3-030-72280-7. ISBN 978-3-030-72279-1. ISSN 1434-9922.
Bach, Sebastian; Binder, Alexander; Montavon, Grégoire; Klauschen, Frederick; Müller, Klaus-Robert; Samek, Wojciech (10 липня 2015). Suarez, Oscar Deniz (ред.). On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation. PLOS ONE. 10 (7): e0130140. Bibcode:2015PLoSO..1030140B. doi:10.1371/journal.pone.0130140. ISSN 1932-6203. PMC 4498753. PMID 26161953.
Martens, David; Provost, Foster (2014). Explaining data-driven document classifications (PDF). MIS Quarterly. 38: 73—99. doi:10.25300/MISQ/2014/38.1.04.
"Why Should I Trust You?" | Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (англ.). doi:10.1145/2939672.2939778.
Le Merrer, Erwan; Trédan, Gilles (September 2020). Remote explainability faces the bouncer problem. Nature Machine Intelligence (англ.). 2 (9): 529—539. arXiv:1910.01432. doi:10.1038/s42256-020-0216-z. ISSN 2522-5839.
Rudin, Cynthia (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence (англ.). 1 (5): 206—215. arXiv:1811.10154. doi:10.1038/s42256-019-0048-x. ISSN 2522-5839.
Singh, Chandan; Nasseri, Keyan; Tan, Yan Shuo; Tang, Tiffany; Yu, Bin (4 травня 2021). imodels: a python package for fitting interpretable models. Journal of Open Source Software (англ.). 6 (61): 3192. Bibcode:2021JOSS....6.3192S. doi:10.21105/joss.03192. ISSN 2475-9066.
Ustun, Berk; Rudin, Cynthia (1 березня 2016). Supersparse linear integer models for optimized medical scoring systems. Machine Learning (англ.). 102 (3): 349—391. doi:10.1007/s10994-015-5528-6. ISSN 1573-0565.
Olah, Chris; Cammarata, Nick; Schubert, Ludwig; Goh, Gabriel; Petrov, Michael; Carter, Shan (10 березня 2020). Zoom In: An Introduction to Circuits. Distill (англ.). 5 (3): e00024.001. doi:10.23915/distill.00024.001. ISSN 2476-0757.
Hendricks, Lisa Anne; Akata, Zeynep; Rohrbach, Marcus; Donahue, Jeff; Schiele, Bernt; Darrell, Trevor (2016). Generating Visual Explanations. Computer Vision – ECCV 2016. Lecture Notes in Computer Science (англ.). Springer International Publishing. 9908: 3—19. arXiv:1603.08507. doi:10.1007/978-3-319-46493-0_1. ISBN 978-3-319-46492-3.
McCoy, Liam G.; Brenna, Connor T. A.; Chen, Stacy S.; Vold, Karina; Das, Sunit (5 листопада 2021). Believing in black boxes: machine learning for healthcare does not need explainability to be evidence-based. Journal of Clinical Epidemiology (English) . 142 (Online ahead of print): 252—257. doi:10.1016/j.jclinepi.2021.11.001. ISSN 0895-4356. PMID 34748907.
Ghassemi, Marzyeh; Oakden-Rayner, Luke; Beam, Andrew L. (1 листопада 2021). The false hope of current approaches to explainable artificial intelligence in health care. The Lancet Digital Health (English) . 3 (11): e745—e750. doi:10.1016/S2589-7500(21)00208-9. ISSN 2589-7500. PMID 34711379.
Weng, Stephen F; Reps, Jenna; Kai, Joe; Garibaldi, Jonathan M; Qureshi, Nadeem (2017). Can machine-learning improve cardiovascular risk prediction using routine clinical data?. PLOS ONE. 12 (4): e0174944. Bibcode:2017PLoSO..1274944W. doi:10.1371/journal.pone.0174944. PMC 5380334. PMID 28376093.
Tosun, Akif (2020). Explainable AI (xAI) for Anatomic Pathology. Advances in Anatomic Pathology. July 2020 - Volume 27 - Issue 4 - p 241-250 (4): 241—250. doi:10.1097/PAP.0000000000000264. PMID 32541594.
Qureshi, M. Atif; Greene, Derek (4 червня 2018). EVE: explainable vector based embedding technique using Wikipedia. Journal of Intelligent Information Systems (англ.). 53: 137—165. arXiv:1702.06891. doi:10.1007/s10844-018-0511-x. ISSN 0925-9902.
Galhotra, Sainyam; Pradhan, Romila; Salimi, Babak (2021). Explaining black-box algorithms using probabilistic contrastive counterfactuals}. ACM Sigmod. 2021: 577—590. arXiv:2103.11972. doi:10.1145/3448016.3458455. ISBN 9781450383431.

dx.doi.org

Tosun, Akif (2020). Explainable AI (xAI) for Anatomic Pathology. Advances in Anatomic Pathology. July 2020 - Volume 27 - Issue 4 - p 241-250 (4): 241—250. doi:10.1097/PAP.0000000000000264. PMID 32541594.

dshieble.github.io

Shiebler, Dan (16 квітня 2017). Understanding Neural Networks with Layerwise Relevance Propagation and Deep Taylor Series. Dan Shiebler. Процитовано 3 листопада 2017.

economist.com

Computer programs recognise white men better than black women. The Economist (англ.). 2018. Процитовано 5 серпня 2018.

fatconference.org

FAT* Conference.

harvard.edu

ui.adsabs.harvard.edu

Rieg, Thilo; Frick, Janek; Baumgartl, Hermann; Buettner, Ricardo (17 грудня 2020). Demonstration of the potential of white-box machine learning approaches to gain insights from cardiovascular disease electrocardiograms. PLOS ONE (англ.). 15 (12): e0243615. Bibcode:2020PLoSO..1543615R. doi:10.1371/journal.pone.0243615. ISSN 1932-6203. PMC 7746264. PMID 33332440.
Murdoch, W. James; Singh, Chandan; Kumbier, Karl; Abbasi-Asl, Reza; Yu, Bin (14 січня 2019). Interpretable machine learning: definitions, methods, and applications. Proceedings of the National Academy of Sciences of the United States of America. 116 (44): 22071—22080. arXiv:1901.04592. Bibcode:2019arXiv190104592M. doi:10.1073/pnas.1900654116. PMC 6825274. PMID 31619572.
Biecek, Przemyslaw (23 червня 2018). DALEX: explainers for complex predictive models. Journal of Machine Learning Research. 19: 1—5. arXiv:1806.08915. Bibcode:2018arXiv180608915B.
Murdoch, W. James; Singh, Chandan; Kumbier, Karl; Abbasi-Asl, Reza; Yu, Bin (14 січня 2019). Interpretable machine learning: definitions, methods, and applications. Proceedings of the National Academy of Sciences of the United States of America. 116 (44): 22071—22080. arXiv:1901.04592. Bibcode:2019arXiv190104592M. doi:10.1073/pnas.1900654116. PMC 6825274. PMID 31619572.
Bach, Sebastian; Binder, Alexander; Montavon, Grégoire; Klauschen, Frederick; Müller, Klaus-Robert; Samek, Wojciech (10 липня 2015). Suarez, Oscar Deniz (ред.). On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation. PLOS ONE. 10 (7): e0130140. Bibcode:2015PLoSO..1030140B. doi:10.1371/journal.pone.0130140. ISSN 1932-6203. PMC 4498753. PMID 26161953.
Lundberg, Scott M; Lee, Su-In (2017), Guyon, I.; Luxburg, U. V.; Bengio, S.; Wallach, H. (ред.), A Unified Approach to Interpreting Model Predictions (PDF), Advances in Neural Information Processing Systems 30, Curran Associates, Inc.: 4765—4774, arXiv:1705.07874, Bibcode:2017arXiv170507874L, процитовано 13 березня 2020
Singh, Chandan; Nasseri, Keyan; Tan, Yan Shuo; Tang, Tiffany; Yu, Bin (4 травня 2021). imodels: a python package for fitting interpretable models. Journal of Open Source Software (англ.). 6 (61): 3192. Bibcode:2021JOSS....6.3192S. doi:10.21105/joss.03192. ISSN 2475-9066.
Weng, Stephen F; Reps, Jenna; Kai, Joe; Garibaldi, Jonathan M; Qureshi, Nadeem (2017). Can machine-learning improve cardiovascular risk prediction using routine clinical data?. PLOS ONE. 12 (4): e0174944. Bibcode:2017PLoSO..1274944W. doi:10.1371/journal.pone.0174944. PMC 5380334. PMID 28376093.

intelligence.org

Bostrom, N., & Yudkowsky, E. (2014). The ethics of artificial intelligence. The Cambridge Handbook of Artificial Intelligence, 316—334.

intelligentrobots.org

IJCAI 2017 Workshop on Explainable Artificial Intelligence (XAI) (PDF). Earthlink. IJCAI. Архів оригіналу (PDF) за 4 квітня 2019. Процитовано 17 липня 2017.

jclinepi.com

McCoy, Liam G.; Brenna, Connor T. A.; Chen, Stacy S.; Vold, Karina; Das, Sunit (5 листопада 2021). Believing in black boxes: machine learning for healthcare does not need explainability to be evidence-based. Journal of Clinical Epidemiology (English) . 142 (Online ahead of print): 252—257. doi:10.1016/j.jclinepi.2021.11.001. ISSN 0895-4356. PMID 34748907.

mipro-proceedings.com

docs.mipro-proceedings.com

Dosilovic, Filip; Brcic, Mario; Hlupic, Nikica (25 травня 2018). Explainable Artificial Intelligence: A Survey (PDF). MIPRO 2018 - 41st International Convention Proceedings. MIPRO 2018. Opatija, Croatia. с. 210—215. doi:10.23919/MIPRO.2018.8400040. ISBN 978-953-233-095-3. Архів оригіналу (PDF) за 10 грудня 2018. Процитовано 9 грудня 2018.

mlr.press

proceedings.mlr.press

Carter, Brandon; Mueller, Jonas; Jain, Siddhartha; Gifford, David (11 квітня 2019). What made you do this? Understanding black-box decisions with sufficient input subsets. The 22nd International Conference on Artificial Intelligence and Statistics (англ.): 567—576.
Shrikumar, Avanti; Greenside, Peyton; Kundaje, Anshul (17 липня 2017). Learning Important Features Through Propagating Activation Differences. International Conference on Machine Learning (англ.): 3145—3153.
Aivodji, Ulrich; Arai, Hiromi; Fortineau, Olivier; Gambs, Sébastien; Hara, Satoshi; Tapp, Alain (24 травня 2019). Fairwashing: the risk of rationalization. International Conference on Machine Learning (англ.). PMLR: 161—170. arXiv:1901.09749.
Vidal, Thibaut; Schiffer, Maximilian (2020). Born-Again Tree Ensembles. International Conference on Machine Learning (англ.). PMLR. 119: 9743—9753. arXiv:2003.11132.
Li, Yixuan; Yosinski, Jason; Clune, Jeff; Lipson, Hod; Hopcroft, John (8 грудня 2015). Convergent Learning: Do different neural networks learn the same representations?. Feature Extraction: Modern Questions and Challenges (англ.). PMLR: 196—212.
Koh, Pang Wei; Liang, Percy (17 липня 2017). Understanding Black-box Predictions via Influence Functions. International Conference on Machine Learning (англ.). PMLR: 1885—1894. arXiv:1703.04730.
Lakkaraju, Himabindu; Rudin, Cynthia. Learning Cost-Effective and Interpretable Treatment Regimes (PDF). International Conference on Artificial Intelligence and Statistics (AISTATS). 2017.

nasa.gov

NASA 'Evolutionary' software automatically designs antenna. NASA. NASA. Архів оригіналу за 8 серпня 2017. Процитовано 17 липня 2017.

nature.com

Le Merrer, Erwan; Trédan, Gilles (September 2020). Remote explainability faces the bouncer problem. Nature Machine Intelligence (англ.). 2 (9): 529—539. arXiv:1910.01432. doi:10.1038/s42256-020-0216-z. ISSN 2522-5839.

newyorker.com

Mukherjee, Siddhartha (27 березня 2017). A.I. Versus M.D. The New Yorker. Процитовано 30 січня 2018.

nih.gov

pubmed.ncbi.nlm.nih.gov

Gunning, D.; Stefik, M.; Choi, J.; Miller, T.; Stumpf, S.; Yang, G.-Z. (18 грудня 2019). XAI-Explainable artificial intelligence. Science Robotics (англ.). 4 (37): eaay7120. doi:10.1126/scirobotics.aay7120. ISSN 2470-9476. PMID 33137719.
Rieg, Thilo; Frick, Janek; Baumgartl, Hermann; Buettner, Ricardo (17 грудня 2020). Demonstration of the potential of white-box machine learning approaches to gain insights from cardiovascular disease electrocardiograms. PLOS ONE (англ.). 15 (12): e0243615. Bibcode:2020PLoSO..1543615R. doi:10.1371/journal.pone.0243615. ISSN 1932-6203. PMC 7746264. PMID 33332440.
Murdoch, W. James; Singh, Chandan; Kumbier, Karl; Abbasi-Asl, Reza; Yu, Bin (14 січня 2019). Interpretable machine learning: definitions, methods, and applications. Proceedings of the National Academy of Sciences of the United States of America. 116 (44): 22071—22080. arXiv:1901.04592. Bibcode:2019arXiv190104592M. doi:10.1073/pnas.1900654116. PMC 6825274. PMID 31619572.
Tickle, A. B.; Andrews, R.; Golea, M.; Diederich, J. (November 1998). The truth will come to light: directions and challenges in extracting the knowledge embedded within trained artificial neural networks. IEEE Transactions on Neural Networks. 9 (6): 1057—1068. doi:10.1109/72.728352. ISSN 1045-9227. PMID 18255792.
Murdoch, W. James; Singh, Chandan; Kumbier, Karl; Abbasi-Asl, Reza; Yu, Bin (14 січня 2019). Interpretable machine learning: definitions, methods, and applications. Proceedings of the National Academy of Sciences of the United States of America. 116 (44): 22071—22080. arXiv:1901.04592. Bibcode:2019arXiv190104592M. doi:10.1073/pnas.1900654116. PMC 6825274. PMID 31619572.
Bach, Sebastian; Binder, Alexander; Montavon, Grégoire; Klauschen, Frederick; Müller, Klaus-Robert; Samek, Wojciech (10 липня 2015). Suarez, Oscar Deniz (ред.). On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation. PLOS ONE. 10 (7): e0130140. Bibcode:2015PLoSO..1030140B. doi:10.1371/journal.pone.0130140. ISSN 1932-6203. PMC 4498753. PMID 26161953.
McCoy, Liam G.; Brenna, Connor T. A.; Chen, Stacy S.; Vold, Karina; Das, Sunit (5 листопада 2021). Believing in black boxes: machine learning for healthcare does not need explainability to be evidence-based. Journal of Clinical Epidemiology (English) . 142 (Online ahead of print): 252—257. doi:10.1016/j.jclinepi.2021.11.001. ISSN 0895-4356. PMID 34748907.
Ghassemi, Marzyeh; Oakden-Rayner, Luke; Beam, Andrew L. (1 листопада 2021). The false hope of current approaches to explainable artificial intelligence in health care. The Lancet Digital Health (English) . 3 (11): e745—e750. doi:10.1016/S2589-7500(21)00208-9. ISSN 2589-7500. PMID 34711379.
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