Inteligência artificial explicável (Portuguese Wikipedia)

Analysis of information sources in references of the Wikipedia article "Inteligência artificial explicável" in Portuguese language version.

refsWebsite

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23doi.org

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15worldcat.org

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12arxiv.org

69^th place

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7mlr.press

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5nih.gov

4^th place

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5harvard.edu

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3theguardian.com

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3ieee.org

652^nd place

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3bloomberg.com

99^th place

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2acm.org

1,185^th place

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2sciencedirect.com

149^th place

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2nature.com

234^th place

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2springer.com

274^th place

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1ssrn.com

703^rd place

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1city.ac.uk

low place

1plos.org

2,112^th place

848^th place

1deepai.org

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1sciencemag.org

857^th place

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1mipro-proceedings.com

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1openai.com

1,559^th place

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1darpa.mil

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1newyorker.com

146^th place

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1dshieble.github.io

low place

1nyu.edu

1,174^th place

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1nips.cc

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1intelligence.org

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1edureka.co

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1fatconference.org

low place

1economist.com

254^th place

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1distill.pub

low place

1intelligentrobots.org

low place

1nasa.gov

75^th place

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1cftc.gov

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1lww.com

2,769^th place

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1spintellx.com

low place

1nytimes.com

7^th place

14^th place

acm.org

dl.acm.org

Lipton, Zachary C. (junho de 2018). «The Mythos of Model Interpretability: In machine learning, the concept of interpretability is both important and slippery.». Queue (em inglês). 16: 31–57. ISSN 1542-7730. doi:10.1145/3236386.3241340
«Axiomatic attribution for deep networks | Proceedings of the 34th International Conference on Machine Learning - Volume 70». dl.acm.org (em inglês). Consultado em 13 de março de 2020

arxiv.org

Rudin, Cynthia (2019). «Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead». Nature Machine Intelligence (em inglês). 1: 206–215. ISSN 2522-5839. arXiv:1811.10154. doi:10.1038/s42256-019-0048-x
Holzinger, Andreas; Plass, Markus (3 de agosto de 2017). «A glass-box interactive machine learning approach for solving NP-hard problems with the human-in-the-loop». arXiv:1708.01104 [cs.AI]
Biecek, Przemyslaw (23 de junho de 2018). «DALEX: explainers for complex predictive models». Journal of Machine Learning Research. 19: 1–5. Bibcode:2018arXiv180608915B. arXiv:1806.08915
Lipton, Zachary C. (10 de junho de 2016). «The Mythos of Model Interpretability». arXiv:1606.03490 [cs.LG]
Murdoch, W. James; Singh, Chandan; Kumbier, Karl; Abbasi-Asl, Reza; Yu, Bin (14 de janeiro de 2019). «Interpretable machine learning: definitions, methods, and applications». Proceedings of the National Academy of Sciences of the United States of America. 116: 22071–22080. Bibcode:2019arXiv190104592M. PMC 6825274. PMID 31619572. arXiv:1901.04592. doi:10.1073/pnas.1900654116
Doshi-Velez, Finale; Kim, Been (27 de fevereiro de 2017). «Towards A Rigorous Science of Interpretable Machine Learning». arXiv:1702.08608 [stat.ML]
Abdollahi, Behnoush, and Olfa Nasraoui. «Explainable Restricted Boltzmann Machines for Collaborative Filtering.». arXiv:1606.07129 [stat.ML]
Lundberg, Scott M; Lee, Su-In (2017), Guyon, I.; Luxburg, U. V.; Bengio, S.; Wallach, H., eds., «A Unified Approach to Interpreting Model Predictions» (PDF), Curran Associates, Inc., Advances in Neural Information Processing Systems 30: 4765–4774, Bibcode:2017arXiv170507874L, arXiv:1705.07874, consultado em 13 de março de 2020
Le Merrer, Erwan; Trédan, Gilles (setembro de 2020). «Remote explainability faces the bouncer problem». Nature Machine Intelligence (em inglês). 2: 529–539. ISSN 2522-5839. arXiv:1910.01432. doi:10.1038/s42256-020-0216-z
Hendricks, Lisa Anne; Akata, Zeynep; Rohrbach, Marcus; Donahue, Jeff; Schiele, Bernt; Darrell, Trevor (2016). «Generating Visual Explanations». Springer International Publishing. Computer Vision – ECCV 2016. Lecture Notes in Computer Science (em inglês). 9908: 3–19. ISBN 978-3-319-46492-3. arXiv:1603.08507. doi:10.1007/978-3-319-46493-0_1
Holzinger, Andreas; Biemann, Chris (28 de dezembro de 2017). «What do we need to build explainable AI systems for the medical domain?». arXiv:1712.09923 [cs.AI]
Qureshi, M. Atif; Greene, Derek (4 de junho de 2018). «EVE: explainable vector based embedding technique using Wikipedia». Journal of Intelligent Information Systems (em inglês). 53: 137–165. ISSN 0925-9902. arXiv:1702.06891. doi:10.1007/s10844-018-0511-x

bloomberg.com

«DeepMind Has Simple Tests That Might Prevent Elon Musk's AI Apocalypse». Bloomberg.com (em inglês). 11 de dezembro de 2017. Consultado em 30 de janeiro de 2018
«Accenture Unveils Tool to Help Companies Insure Their AI Is Fair». Bloomberg.com (em inglês). Junho de 2018. Consultado em 5 de agosto de 2018
Kahn, Jeremy (12 de dezembro de 2018). «Artificial Intelligence Has Some Explaining to Do». Bloomberg Businessweek. Consultado em 17 de dezembro de 2018

cftc.gov

«The Flash Crash: The Impact of High Frequency Trading on an Electronic Market» (PDF). CFTC. CFTC. Consultado em 17 de julho de 2017

city.ac.uk

openaccess.city.ac.uk

Gunning, D.; Stefik, M.; Choi, J.; Miller, T.; Stumpf, S.; Yang, G.-Z. (18 de dezembro de 2019). «XAI-Explainable artificial intelligence». Science Robotics (em inglês). 4: eaay7120. ISSN 2470-9476. doi:10.1126/scirobotics.aay7120

darpa.mil

«Explainable Artificial Intelligence (XAI)». DARPA. DARPA. Consultado em 17 de julho de 2017

deepai.org

«Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI». DeepAI. 22 de outubro de 2019. Consultado em 13 de janeiro de 2021

distill.pub

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

doi.org

dx.doi.org

Gunning, D.; Stefik, M.; Choi, J.; Miller, T.; Stumpf, S.; Yang, G.-Z. (18 de dezembro de 2019). «XAI-Explainable artificial intelligence». Science Robotics (em inglês). 4: eaay7120. ISSN 2470-9476. doi:10.1126/scirobotics.aay7120
Rieg, Thilo; Frick, Janek; Baumgartl, Hermann; Buettner, Ricardo (17 de dezembro de 2020). «Demonstration of the potential of white-box machine learning approaches to gain insights from cardiovascular disease electrocardiograms». PLOS ONE (em inglês). 15: e0243615. ISSN 1932-6203. PMC 7746264. PMID 33332440. doi:10.1371/journal.pone.0243615
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. ISSN 2169-3536. doi:10.1109/ACCESS.2019.2949286
Roscher, R.; Bohn, B.; Duarte, M. F.; Garcke, J. (2020). «Explainable Machine Learning for Scientific Insights and Discoveries». IEEE Access. 8: 42200–42216. ISSN 2169-3536. doi:10.1109/ACCESS.2020.2976199
Lipton, Zachary C. (junho de 2018). «The Mythos of Model Interpretability: In machine learning, the concept of interpretability is both important and slippery.». Queue (em inglês). 16: 31–57. ISSN 1542-7730. doi:10.1145/3236386.3241340
Montavon, Grégoire; Samek, Wojciech; Müller, Klaus-Robert (1 de fevereiro de 2018). «Methods for interpreting and understanding deep neural networks». Digital Signal Processing (em inglês). 73: 1–15. ISSN 1051-2004. doi:10.1016/j.dsp.2017.10.011
Adadi, A.; Berrada, M. (2018). «Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)». IEEE Access. 6: 52138–52160. ISSN 2169-3536. doi:10.1109/ACCESS.2018.2870052
Rudin, Cynthia (2019). «Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead». Nature Machine Intelligence (em inglês). 1: 206–215. ISSN 2522-5839. arXiv:1811.10154. doi:10.1038/s42256-019-0048-x
Dosilovic, Filip; Brcic, Mario; Hlupic, Nikica (25 de maio de 2018). Explainable Artificial Intelligence: A Survey (PDF). MIPRO 2018. Opatija, Croatia. pp. 210–215. doi:10.23919/MIPRO.2018.8400040
Aler Tubella, Andrea; Theodorou, Andreas; Dignum, Frank; Dignum, Virginia (2019). Governance by Glass-Box: Implementing Transparent Moral Bounds for AI Behaviour. California: International Joint Conferences on Artificial Intelligence Organization. ISBN 978-0-9992411-4-1. doi:10.24963/ijcai.2019/802
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. [S.l.: s.n.] pp. 5868–5870. ISBN 9780999241127. doi:10.24963/ijcai.2018/865
Tickle, A. B.; Andrews, R.; Golea, M.; Diederich, J. (novembro de 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: 1057–1068. ISSN 1045-9227. PMID 18255792. doi:10.1109/72.728352
Csiszár, Orsolya; Csiszár, Gábor; Dombi, József (8 de julho de 2020). «Interpretable neural networks based on continuous-valued logic and multicriteria decision operators». Knowledge-Based Systems (em inglês). 199. 105972 páginas. ISSN 0950-7051. doi:10.1016/j.knosys.2020.105972
Murdoch, W. James; Singh, Chandan; Kumbier, Karl; Abbasi-Asl, Reza; Yu, Bin (14 de janeiro de 2019). «Interpretable machine learning: definitions, methods, and applications». Proceedings of the National Academy of Sciences of the United States of America. 116: 22071–22080. Bibcode:2019arXiv190104592M. PMC 6825274. PMID 31619572. arXiv:1901.04592. doi:10.1073/pnas.1900654116
Dombi, József; Csiszár, Orsolya (2021). «Explainable Neural Networks Based on Fuzzy Logic and Multi-criteria Decision Tools». Studies in Fuzziness and Soft Computing (em inglês). ISSN 1434-9922. doi:10.1007/978-3-030-72280-7
Bach, Sebastian; Binder, Alexander; Montavon, Grégoire; Klauschen, Frederick; Müller, Klaus-Robert; Samek, Wojciech (10 de julho de 2015). «On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation». PLOS ONE. 10: e0130140. Bibcode:2015PLoSO..1030140B. ISSN 1932-6203. PMC 4498753. PMID 26161953. doi:10.1371/journal.pone.0130140
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» (em inglês). doi:10.1145/2939672.2939778
Le Merrer, Erwan; Trédan, Gilles (setembro de 2020). «Remote explainability faces the bouncer problem». Nature Machine Intelligence (em inglês). 2: 529–539. ISSN 2522-5839. arXiv:1910.01432. doi:10.1038/s42256-020-0216-z
Olah, Chris; Cammarata, Nick; Schubert, Ludwig; Goh, Gabriel; Petrov, Michael; Carter, Shan (10 de março de 2020). «Zoom In: An Introduction to Circuits». Distill (em inglês). 5: e00024.001. ISSN 2476-0757. doi:10.23915/distill.00024.001
Hendricks, Lisa Anne; Akata, Zeynep; Rohrbach, Marcus; Donahue, Jeff; Schiele, Bernt; Darrell, Trevor (2016). «Generating Visual Explanations». Springer International Publishing. Computer Vision – ECCV 2016. Lecture Notes in Computer Science (em inglês). 9908: 3–19. ISBN 978-3-319-46492-3. arXiv:1603.08507. doi:10.1007/978-3-319-46493-0_1
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: e0174944. Bibcode:2017PLoSO..1274944W. PMC 5380334. PMID 28376093. doi:10.1371/journal.pone.0174944
Qureshi, M. Atif; Greene, Derek (4 de junho de 2018). «EVE: explainable vector based embedding technique using Wikipedia». Journal of Intelligent Information Systems (em inglês). 53: 137–165. ISSN 0925-9902. arXiv:1702.06891. doi:10.1007/s10844-018-0511-x

dshieble.github.io

Shiebler, Dan (16 de abril de 2017). «Understanding Neural Networks with Layerwise Relevance Propagation and Deep Taylor Series». Dan Shiebler. Consultado em 3 de novembro de 2017

economist.com

«Computer programs recognise white men better than black women». The Economist (em inglês). 2018. Consultado em 5 de agosto de 2018

edureka.co

says, Charlotte Lancaster (4 de outubro de 2018). «What is Artificial Intelligence | Artificial Intelligence Explained». Edureka (em inglês). Consultado em 14 de setembro de 2020

fatconference.org

«FAT* Conference»

harvard.edu

ui.adsabs.harvard.edu

Biecek, Przemyslaw (23 de junho de 2018). «DALEX: explainers for complex predictive models». Journal of Machine Learning Research. 19: 1–5. Bibcode:2018arXiv180608915B. arXiv:1806.08915
Murdoch, W. James; Singh, Chandan; Kumbier, Karl; Abbasi-Asl, Reza; Yu, Bin (14 de janeiro de 2019). «Interpretable machine learning: definitions, methods, and applications». Proceedings of the National Academy of Sciences of the United States of America. 116: 22071–22080. Bibcode:2019arXiv190104592M. PMC 6825274. PMID 31619572. arXiv:1901.04592. doi:10.1073/pnas.1900654116
Bach, Sebastian; Binder, Alexander; Montavon, Grégoire; Klauschen, Frederick; Müller, Klaus-Robert; Samek, Wojciech (10 de julho de 2015). «On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation». PLOS ONE. 10: e0130140. Bibcode:2015PLoSO..1030140B. ISSN 1932-6203. PMC 4498753. PMID 26161953. doi:10.1371/journal.pone.0130140
Lundberg, Scott M; Lee, Su-In (2017), Guyon, I.; Luxburg, U. V.; Bengio, S.; Wallach, H., eds., «A Unified Approach to Interpreting Model Predictions» (PDF), Curran Associates, Inc., Advances in Neural Information Processing Systems 30: 4765–4774, Bibcode:2017arXiv170507874L, arXiv:1705.07874, consultado em 13 de março de 2020
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: e0174944. Bibcode:2017PLoSO..1274944W. PMC 5380334. PMID 28376093. doi:10.1371/journal.pone.0174944

ieee.org

ieeexplore.ieee.org

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. ISSN 2169-3536. doi:10.1109/ACCESS.2019.2949286
Roscher, R.; Bohn, B.; Duarte, M. F.; Garcke, J. (2020). «Explainable Machine Learning for Scientific Insights and Discoveries». IEEE Access. 8: 42200–42216. ISSN 2169-3536. doi:10.1109/ACCESS.2020.2976199
Adadi, A.; Berrada, M. (2018). «Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)». IEEE Access. 6: 52138–52160. ISSN 2169-3536. doi:10.1109/ACCESS.2018.2870052

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. Consultado em 17 de julho de 2017

lww.com

journals.lww.com

Tosun, Akif. «Explainable AI (xAI) for Anatomic Pathology». Advances in Anatomic Pathology. July 2020 - Volume 27 - Issue 4 - p 241-250

mipro-proceedings.com

docs.mipro-proceedings.com

Dosilovic, Filip; Brcic, Mario; Hlupic, Nikica (25 de maio de 2018). Explainable Artificial Intelligence: A Survey (PDF). MIPRO 2018. Opatija, Croatia. pp. 210–215. doi:10.23919/MIPRO.2018.8400040

mlr.press

proceedings.mlr.press

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

nature.com

Rudin, Cynthia (2019). «Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead». Nature Machine Intelligence (em inglês). 1: 206–215. ISSN 2522-5839. arXiv:1811.10154. doi:10.1038/s42256-019-0048-x
Le Merrer, Erwan; Trédan, Gilles (setembro de 2020). «Remote explainability faces the bouncer problem». Nature Machine Intelligence (em inglês). 2: 529–539. ISSN 2522-5839. arXiv:1910.01432. doi:10.1038/s42256-020-0216-z

newyorker.com

Mukherjee, Siddhartha (27 de março de 2017). «A.I. Versus M.D.». The New Yorker. Consultado em 30 de janeiro de 2018

nih.gov

ncbi.nlm.nih.gov

Rieg, Thilo; Frick, Janek; Baumgartl, Hermann; Buettner, Ricardo (17 de dezembro de 2020). «Demonstration of the potential of white-box machine learning approaches to gain insights from cardiovascular disease electrocardiograms». PLOS ONE (em inglês). 15: e0243615. ISSN 1932-6203. PMC 7746264. PMID 33332440. doi:10.1371/journal.pone.0243615
Tickle, A. B.; Andrews, R.; Golea, M.; Diederich, J. (novembro de 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: 1057–1068. ISSN 1045-9227. PMID 18255792. doi:10.1109/72.728352
Murdoch, W. James; Singh, Chandan; Kumbier, Karl; Abbasi-Asl, Reza; Yu, Bin (14 de janeiro de 2019). «Interpretable machine learning: definitions, methods, and applications». Proceedings of the National Academy of Sciences of the United States of America. 116: 22071–22080. Bibcode:2019arXiv190104592M. PMC 6825274. PMID 31619572. arXiv:1901.04592. doi:10.1073/pnas.1900654116
Bach, Sebastian; Binder, Alexander; Montavon, Grégoire; Klauschen, Frederick; Müller, Klaus-Robert; Samek, Wojciech (10 de julho de 2015). «On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation». PLOS ONE. 10: e0130140. Bibcode:2015PLoSO..1030140B. ISSN 1932-6203. PMC 4498753. PMID 26161953. doi:10.1371/journal.pone.0130140
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: e0174944. Bibcode:2017PLoSO..1274944W. PMC 5380334. PMID 28376093. doi:10.1371/journal.pone.0174944

nips.cc

papers.nips.cc

Lundberg, Scott M; Lee, Su-In (2017), Guyon, I.; Luxburg, U. V.; Bengio, S.; Wallach, H., eds., «A Unified Approach to Interpreting Model Predictions» (PDF), Curran Associates, Inc., Advances in Neural Information Processing Systems 30: 4765–4774, Bibcode:2017arXiv170507874L, arXiv:1705.07874, consultado em 13 de março de 2020

nytimes.com

Abrams, Rachel; Kurtz, Annalyn (julho de 2016). «Joshua Brown, Who Died in Self-Driving Accident, Tested Limits of His Tesla». New York Times. Consultado em 17 de julho de 2017

nyu.edu

pages.stern.nyu.edu

Martens, David; Provost, Foster (2014). «Explaining data-driven document classifications» (PDF). MIS Quarterly. 38: 73–99. doi:10.25300/MISQ/2014/38.1.04

openai.com

blog.openai.com

«Learning from Human Preferences». OpenAI Blog. 13 de junho de 2017. Consultado em 30 de janeiro de 2018

plos.org

journals.plos.org

Rieg, Thilo; Frick, Janek; Baumgartl, Hermann; Buettner, Ricardo (17 de dezembro de 2020). «Demonstration of the potential of white-box machine learning approaches to gain insights from cardiovascular disease electrocardiograms». PLOS ONE (em inglês). 15: e0243615. ISSN 1932-6203. PMC 7746264. PMID 33332440. doi:10.1371/journal.pone.0243615

sciencedirect.com

Montavon, Grégoire; Samek, Wojciech; Müller, Klaus-Robert (1 de fevereiro de 2018). «Methods for interpreting and understanding deep neural networks». Digital Signal Processing (em inglês). 73: 1–15. ISSN 1051-2004. doi:10.1016/j.dsp.2017.10.011
Csiszár, Orsolya; Csiszár, Gábor; Dombi, József (8 de julho de 2020). «Interpretable neural networks based on continuous-valued logic and multicriteria decision operators». Knowledge-Based Systems (em inglês). 199. 105972 páginas. ISSN 0950-7051. doi:10.1016/j.knosys.2020.105972

sciencemag.org

«How AI detectives are cracking open the black box of deep learning». Science (em inglês). 5 de julho de 2017. Consultado em 30 de janeiro de 2018 .

spintellx.com

https://www.spintellx.com/

springer.com

link.springer.com

Dombi, József; Csiszár, Orsolya (2021). «Explainable Neural Networks Based on Fuzzy Logic and Multi-criteria Decision Tools». Studies in Fuzziness and Soft Computing (em inglês). ISSN 1434-9922. doi:10.1007/978-3-030-72280-7
Hendricks, Lisa Anne; Akata, Zeynep; Rohrbach, Marcus; Donahue, Jeff; Schiele, Bernt; Darrell, Trevor (2016). «Generating Visual Explanations». Springer International Publishing. Computer Vision – ECCV 2016. Lecture Notes in Computer Science (em inglês). 9908: 3–19. ISBN 978-3-319-46492-3. arXiv:1603.08507. doi:10.1007/978-3-319-46493-0_1

ssrn.com

Edwards, Lilian; Veale, Michael (2017). «Slave to the Algorithm? Why a 'Right to an Explanation' Is Probably Not the Remedy You Are Looking For». Duke Law and Technology Review. 16. 18 páginas. SSRN 2972855

theguardian.com

Sample, Ian (5 de novembro de 2017). «Computer says no: why making AIs fair, accountable and transparent is crucial». The Guardian (em inglês). Consultado em 30 de janeiro de 2018
Sample, Ian (5 de novembro de 2017). «Computer says no: why making AIs fair, accountable and transparent is crucial». The Guardian (em inglês). Consultado em 5 de agosto de 2018
«Tesla says it has 'no way of knowing' if autopilot was used in fatal Chinese crash». Guardian. 14 de setembro de 2016. Consultado em 17 de julho de 2017

worldcat.org

Gunning, D.; Stefik, M.; Choi, J.; Miller, T.; Stumpf, S.; Yang, G.-Z. (18 de dezembro de 2019). «XAI-Explainable artificial intelligence». Science Robotics (em inglês). 4: eaay7120. ISSN 2470-9476. doi:10.1126/scirobotics.aay7120
Rieg, Thilo; Frick, Janek; Baumgartl, Hermann; Buettner, Ricardo (17 de dezembro de 2020). «Demonstration of the potential of white-box machine learning approaches to gain insights from cardiovascular disease electrocardiograms». PLOS ONE (em inglês). 15: e0243615. ISSN 1932-6203. PMC 7746264. PMID 33332440. doi:10.1371/journal.pone.0243615
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