הסברתיות בינה מלאכותית (Hebrew Wikipedia)

Analysis of information sources in references of the Wikipedia article "הסברתיות בינה מלאכותית" in Hebrew language version.

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49doi.org

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7mlr.press

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5ieee.org

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5acm.org

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5arxiv.org

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4mdpi.com

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3bloomberg.com

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3springer.com

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2nature.com

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2worldcat.org

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2plos.org

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1ercim.eu

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1degruyter.com

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1science.org

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1deepai.org

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1medrxiv.org

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1darpa.mil

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1ijcai.org

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1stanford.edu

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1google.co.il

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1aaai.org

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1misq.org

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1neurips.cc

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1theoj.org

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1economist.com

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1distill.pub

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1analyticsindiamag.com

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1codemotion.com

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1web.archive.org

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1sagepub.com

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1aom.org

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1gi.de

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aaai.org

cdn.aaai.org

Van Lent, M., Fisher, W., & Mancuso, M., An explainable artificial intelligence system for small-unit tactical behavior, Proceedings of the national conference on artificial intelligence, 2004, עמ' 900-907

acm.org

dl.acm.org

Zachary C. Lipton, The Mythos of Model Interpretability: In machine learning, the concept of interpretability is both important and slippery., Queue 16, 2018-06, עמ' 31–57 doi: 10.1145/3236386.3241340
Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin, "Why Should I Trust You?": Explaining the Predictions of Any Classifier, ACM, 2016-08-13, עמ' 1135–1144 doi: 10.1145/2939672.2939778
Umang Bhatt, Alice Xiang, Shubham Sharma, Adrian Weller, Ankur Taly, Yunhan Jia, Joydeep Ghosh, Ruchir Puri, José M. F. Moura, Peter Eckersley, Explainable machine learning in deployment, ACM, 2020-01-27, עמ' 648–657 doi: 10.1145/3351095.3375624
Michael Veale, Max Van Kleek, Reuben Binns, Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making, ACM, 2018-04-21, עמ' 1–14 doi: 10.1145/3173574.3174014
Hao-Fei Cheng, Ruotong Wang, Zheng Zhang, Fiona O'Connell, Terrance Gray, F. Maxwell Harper, Haiyi Zhu, Explaining Decision-Making Algorithms through UI: Strategies to Help Non-Expert Stakeholders, ACM, 2019-05-02, עמ' 1–12 doi: 10.1145/3290605.3300789

analyticsindiamag.com

Yugesh Verma, A Complete Guide to SHAP - SHAPley Additive exPlanations for Practitioners, Analytics India Magazine, ‏2021-12-25 (באנגלית אמריקאית)

aom.org

journals.aom.org

Yotam Liel, Lior Zalmanson, Turning Off Your Better Judgment – Conformity to Algorithmic Recommendations, Academy of Management Proceedings 2023, 2023-08 doi: 10.5465/AMPROC.2023.277bp

arxiv.org

Ludan, J. M., Lyu, Q., Yang, Y., Dugan, L., Yatskar, M., & Callison-Burch, C., Interpretable-by-Design Text Classification with Iteratively Generated Concept Bottleneck, 2023
Przemyslaw Biecek, DALEX: explainers for complex predictive models, 2018 doi: 10.48550/ARXIV.1806.08915
Doshi-Velez, F., & Kim, B., Towards a rigorous science of interpretable machine learning, arXiv preprint, 2017
Behnoush Abdollahi, Olfa Nasraoui, Explainable Restricted Boltzmann Machines for Collaborative Filtering, arXiv preprint, 2016
Advait Sarkar, Is explainable AI a race against model complexity?, arXiv preprint, 2022

bloomberg.com

Bloomberg - Are you a robot?, www.bloomberg.com
Bloomberg - Are you a robot?, www.bloomberg.com
"Artificial Intelligence Has Some Explaining to Do". Bloomberg.com (באנגלית). 2018-12-12. נבדק ב-2024-01-23.

codemotion.com

Denis Rothman, Exploring LIME Explanations and the Mathematics Behind It, Codemotion Magazine, ‏2020-10-07 (באנגלית אמריקאית)

darpa.mil

Explainable Artificial Intelligence (XAI), www.darpa.mil

deepai.org

Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI, DeepAI, ‏2019-10-22

degruyter.com

Fatemeh Alizadeh, Gunnar Stevens, Margarita Esau, 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, i-com 20, 2021-04-27, עמ' 3–17 doi: 10.1515/icom-2021-0009

distill.pub

Chris Olah, Nick Cammarata, Ludwig Schubert, Gabriel Goh, Michael Petrov, Shan Carter, Zoom In: An Introduction to Circuits, Distill 5, 2020-03-10 doi: 10.23915/distill.00024.001

doi.org

dx.doi.org

P Jonathon Phillips, Carina A Hahn, Peter C Fontana, Amy N Yates, Kristen Greene, David A Broniatowski, Mark A Przybocki, Four principles of explainable artificial intelligence, 2021-09-29 doi: 10.6028/nist.ir.8312
Giulia Vilone, Luca Longo, Notions of explainability and evaluation approaches for explainable artificial intelligence, Information Fusion 76, 2021-12, עמ' 89–106 doi: 10.1016/j.inffus.2021.05.009
Davide Castelvecchi, Can we open the black box of AI?, Nature 538, 2016-10, עמ' 20–23 doi: 10.1038/538020a
Fatemeh Alizadeh, Gunnar Stevens, Margarita Esau, 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, i-com 20, 2021-04-27, עמ' 3–17 doi: 10.1515/icom-2021-0009
Lilian Edwards, Michael Veale, Slave to the Algorithm? Why a 'right to an explanation' is probably not the remedy you are looking for, dx.doi.org, ‏2017-11-18
David Gunning, Mark Stefik, Jaesik Choi, Timothy Miller, Simone Stumpf, Guang-Zhong Yang, XAI—Explainable artificial intelligence, Science Robotics 4, 2019-12-18 doi: 10.1126/scirobotics.aay7120
Thilo Rieg, Janek Frick, Hermann Baumgartl, Ricardo Buettner, Demonstration of the potential of white-box machine learning approaches to gain insights from cardiovascular disease electrocardiograms, PLOS ONE 15, 2020-12-17, עמ' e0243615 doi: 10.1371/journal.pone.0243615
Giulia Vilone, Luca Longo, Classification of Explainable Artificial Intelligence Methods through Their Output Formats, Machine Learning and Knowledge Extraction 3, 2021-08-04, עמ' 615–661 doi: 10.3390/make3030032
Octavio Loyola-Gonzalez, Black-Box vs. White-Box: Understanding Their Advantages and Weaknesses From a Practical Point of View, IEEE Access 7, 2019, עמ' 154096–154113 doi: 10.1109/ACCESS.2019.2949286
Ribana Roscher, Bastian Bohn, Marco F. Duarte, Jochen Garcke, Explainable Machine Learning for Scientific Insights and Discoveries, IEEE Access 8, 2020, עמ' 42200–42216 doi: 10.1109/ACCESS.2020.2976199
W. James Murdoch, Chandan Singh, Karl Kumbier, Reza Abbasi-Asl, Bin Yu, Definitions, methods, and applications in interpretable machine learning, Proceedings of the National Academy of Sciences 116, 2019-10-29, עמ' 22071–22080 doi: 10.1073/pnas.1900654116
Zachary C. Lipton, The Mythos of Model Interpretability: In machine learning, the concept of interpretability is both important and slippery., Queue 16, 2018-06, עמ' 31–57 doi: 10.1145/3236386.3241340
Grégoire Montavon, Wojciech Samek, Klaus-Robert Müller, Methods for interpreting and understanding deep neural networks, Digital Signal Processing 73, 2018-02, עמ' 1–15 doi: 10.1016/j.dsp.2017.10.011
Amina Adadi, Mohammed Berrada, Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI), IEEE Access 6, 2018, עמ' 52138–52160 doi: 10.1109/ACCESS.2018.2870052
Cynthia Rudin, Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead, Nature Machine Intelligence 1, 2019-05-13, עמ' 206–215 doi: 10.1038/s42256-019-0048-x
Simon Wenninger, Can Kaymakci, Christian Wiethe, Explainable long-term building energy consumption prediction using QLattice, Applied Energy 308, 2022-02, עמ' 118300 doi: 10.1016/j.apenergy.2021.118300
Michael Christiansen, Casper Wilstrup, Paula L. Hedley, Explainable “white-box” machine learning is the way forward in preeclampsia screening, American Journal of Obstetrics and Gynecology 227, 2022-11, עמ' 791 doi: 10.1016/j.ajog.2022.06.057
Casper Wilstup, Chris Cave, Combining symbolic regression with the Cox proportional hazards model improves prediction of heart failure deaths, 2021-01-15 doi: 10.1101/2021.01.15.21249874
How AI detectives are cracking open the black box of deep learning, AAAS Articles DO Group, ‏2021-03-28
Filip Karlo Dosilovic, Mario Brcic, Nikica Hlupic, Explainable artificial intelligence: A survey, IEEE, 2018-05, עמ' 0210–0215 doi: 10.23919/MIPRO.2018.8400040
Jose Bernal, Claudia Mazo, Transparency of Artificial Intelligence in Healthcare: Insights from Professionals in Computing and Healthcare Worldwide, Applied Sciences 12, 2022-10-11, עמ' 10228 doi: 10.3390/app122010228
Anna Markella Antoniadi, Yuhan Du, Yasmine Guendouz, Lan Wei, Claudia Mazo, Brett A. Becker, Catherine Mooney, Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review, Applied Sciences 11, 2021-05-31, עמ' 5088 doi: 10.3390/app11115088
Przemyslaw Biecek, DALEX: explainers for complex predictive models, 2018 doi: 10.48550/ARXIV.1806.08915
Arun Rai, Explainable AI: from black box to glass box, Journal of the Academy of Marketing Science 48, 2019-12-17, עמ' 137–141 doi: 10.1007/s11747-019-00710-5
Kacper Sokol, Peter Flach, Glass-Box: Explaining AI Decisions With Counterfactual Statements Through Conversation With a Voice-enabled Virtual Assistant, International Joint Conferences on Artificial Intelligence Organization, 2018-07, עמ' 5868–5870 doi: 10.24963/ijcai.2018/865
David A Broniatowski, Psychological foundations of explainability and interpretability in artificial intelligence, 2021-04-12 doi: 10.6028/nist.ir.8367
Software and systems engineering. Software testing, BSI British Standards, נבדק ב-2024-01-22
A.B. Tickle, R. Andrews, M. Golea, J. Diederich, 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, Nov./1998, עמ' 1057–1068 doi: 10.1109/72.728352
Marvin Minsky, Ray Kurzweil, Steve Mann, The society of intelligent veillance, 2013 IEEE International Symposium on Technology and Society (ISTAS): Social Implications of Wearable Computing and Augmediated Reality in Everyday Life, IEEE, 2013-06 doi: 10.1109/istas.2013.6613095
Orsolya Csiszár, Gábor Csiszár, József Dombi, Interpretable neural networks based on continuous-valued logic and multicriteria decision operators, Knowledge-Based Systems 199, 2020-07, עמ' 105972 doi: 10.1016/j.knosys.2020.105972
Sebastian Bach, Alexander Binder, Grégoire Montavon, Frederick Klauschen, Klaus-Robert Müller, Wojciech Samek, On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation, PLOS ONE 10, 2015-07-10, עמ' e0130140 doi: 10.1371/journal.pone.0130140
University of Antwerp, David Martens, Foster Provost, New York University, Explaining Data-Driven Document Classifications, MIS Quarterly 38, 2014-01-01, עמ' 73–99 doi: 10.25300/MISQ/2014/38.1.04
Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin, "Why Should I Trust You?": Explaining the Predictions of Any Classifier, ACM, 2016-08-13, עמ' 1135–1144 doi: 10.1145/2939672.2939778
Chandan Singh, Keyan Nasseri, Yan Tan, Tiffany Tang, Bin Yu, imodels: a python package for fitting interpretable models, Journal of Open Source Software 6, 2021-05-04, עמ' 3192 doi: 10.21105/joss.03192
Berk Ustun, Cynthia Rudin, Supersparse linear integer models for optimized medical scoring systems, Machine Learning 102, 2016-03, עמ' 349–391 doi: 10.1007/s10994-015-5528-6
Nick Bostrom, Eliezer Yudkowsky, The ethics of artificial intelligence, Cambridge University Press, 2014-06-12, עמ' 316–334
Chris Olah, Nick Cammarata, Ludwig Schubert, Gabriel Goh, Michael Petrov, Shan Carter, Zoom In: An Introduction to Circuits, Distill 5, 2020-03-10 doi: 10.23915/distill.00024.001
Philip Gouverneur, Frédéric Li, Kimiaki Shirahama, Luisa Luebke, Wacław M. Adamczyk, Tibor M. Szikszay, Kerstin Luedtke, Marcin Grzegorzek, Explainable Artificial Intelligence (XAI) in Pain Research: Understanding the Role of Electrodermal Activity for Automated Pain Recognition, Sensors 23, 2023-02-09, עמ' 1959 doi: 10.3390/s23041959
Adrien Bennetot, Ivan Donadello, Ayoub El Qadi, Mauro Dragoni, Thomas Frossard, Benedikt Wagner, Anna Saranti, Silvia Tulli, Maria Trocan, Raja Chatila, Andreas Holzinger, Artur d'Avila Garcez, Natalia Diaz Rodriguez, A Practical Guide on Explainable Ai Techniques Applied on Biomedical Use Case Applications, SSRN Electronic Journal, 2022 doi: 10.2139/ssrn.4229624
Umang Bhatt, Alice Xiang, Shubham Sharma, Adrian Weller, Ankur Taly, Yunhan Jia, Joydeep Ghosh, Ruchir Puri, José M. F. Moura, Peter Eckersley, Explainable machine learning in deployment, ACM, 2020-01-27, עמ' 648–657 doi: 10.1145/3351095.3375624
Jenna Burrell, How the machine ‘thinks’: Understanding opacity in machine learning algorithms, Big Data & Society 3, 2016-06-01, עמ' 205395171562251 doi: 10.1177/2053951715622512
Michael Veale, Max Van Kleek, Reuben Binns, Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making, ACM, 2018-04-21, עמ' 1–14 doi: 10.1145/3173574.3174014
Tongxi Hu, Xuesong Zhang, Gil Bohrer, Yanlan Liu, Yuyu Zhou, Jay Martin, Yang Li, Kaiguang Zhao, Crop yield prediction via explainable AI and interpretable machine learning: Dangers of black box models for evaluating climate change impacts on crop yield, Agricultural and Forest Meteorology 336, 2023-06, עמ' 109458 doi: 10.1016/j.agrformet.2023.109458
Hao-Fei Cheng, Ruotong Wang, Zheng Zhang, Fiona O'Connell, Terrance Gray, F. Maxwell Harper, Haiyi Zhu, Explaining Decision-Making Algorithms through UI: Strategies to Help Non-Expert Stakeholders, ACM, 2019-05-02, עמ' 1–12 doi: 10.1145/3290605.3300789
Yotam Liel, Lior Zalmanson, Turning Off Your Better Judgment – Conformity to Algorithmic Recommendations, Academy of Management Proceedings 2023, 2023-08 doi: 10.5465/AMPROC.2023.277bp
Liam G. McCoy, Connor T.A. Brenna, Stacy S. Chen, Karina Vold, Sunit Das, Believing in black boxes: machine learning for healthcare does not need explainability to be evidence-based, Journal of Clinical Epidemiology 142, 2022-02, עמ' 252–257 doi: 10.1016/j.jclinepi.2021.11.001
Marzyeh Ghassemi, Luke Oakden-Rayner, Andrew L Beam, The false hope of current approaches to explainable artificial intelligence in health care, The Lancet Digital Health 3, 2021-11, עמ' e745–e750 doi: 10.1016/S2589-7500(21)00208-9
Fatemeh Alizadeh, Margarita Esau, Gunnar Stevens, Lena Cassens, eXplainable AI: Take one Step Back, Move two Steps forward, 2020 doi: 10.18420/MUC2020-WS111-369

doi.org

David A Broniatowski, Psychological foundations of explainability and interpretability in artificial intelligence, 2021-04-12 doi: 10.6028/nist.ir.8367

economist.com

"Computer programs recognise white men better than black women". The Economist. ISSN 0013-0613. נבדק ב-2024-01-22.

elsevier.com

linkinghub.elsevier.com

Giulia Vilone, Luca Longo, Notions of explainability and evaluation approaches for explainable artificial intelligence, Information Fusion 76, 2021-12, עמ' 89–106 doi: 10.1016/j.inffus.2021.05.009
Grégoire Montavon, Wojciech Samek, Klaus-Robert Müller, Methods for interpreting and understanding deep neural networks, Digital Signal Processing 73, 2018-02, עמ' 1–15 doi: 10.1016/j.dsp.2017.10.011
Simon Wenninger, Can Kaymakci, Christian Wiethe, Explainable long-term building energy consumption prediction using QLattice, Applied Energy 308, 2022-02, עמ' 118300 doi: 10.1016/j.apenergy.2021.118300
Michael Christiansen, Casper Wilstrup, Paula L. Hedley, Explainable “white-box” machine learning is the way forward in preeclampsia screening, American Journal of Obstetrics and Gynecology 227, 2022-11, עמ' 791 doi: 10.1016/j.ajog.2022.06.057
Orsolya Csiszár, Gábor Csiszár, József Dombi, Interpretable neural networks based on continuous-valued logic and multicriteria decision operators, Knowledge-Based Systems 199, 2020-07, עמ' 105972 doi: 10.1016/j.knosys.2020.105972
Tongxi Hu, Xuesong Zhang, Gil Bohrer, Yanlan Liu, Yuyu Zhou, Jay Martin, Yang Li, Kaiguang Zhao, Crop yield prediction via explainable AI and interpretable machine learning: Dangers of black box models for evaluating climate change impacts on crop yield, Agricultural and Forest Meteorology 336, 2023-06, עמ' 109458 doi: 10.1016/j.agrformet.2023.109458
Liam G. McCoy, Connor T.A. Brenna, Stacy S. Chen, Karina Vold, Sunit Das, Believing in black boxes: machine learning for healthcare does not need explainability to be evidence-based, Journal of Clinical Epidemiology 142, 2022-02, עמ' 252–257 doi: 10.1016/j.jclinepi.2021.11.001
Marzyeh Ghassemi, Luke Oakden-Rayner, Andrew L Beam, The false hope of current approaches to explainable artificial intelligence in health care, The Lancet Digital Health 3, 2021-11, עמ' e745–e750 doi: 10.1016/S2589-7500(21)00208-9

ercim.eu

ercim-news.ercim.eu

Mihály, Héder, Explainable AI: A Brief History of the Concept, ERCIM News 134, עמ' 9–10

gi.de

dl.gi.de

Fatemeh Alizadeh, Margarita Esau, Gunnar Stevens, Lena Cassens, eXplainable AI: Take one Step Back, Move two Steps forward, 2020 doi: 10.18420/MUC2020-WS111-369

google.co.il

books.google.co.il

Ray Bareiss, Exemplar-Based Knowledge Acquisition: A Unified Approach to Concept Representation, Classification, and Learning, Academic Press, 2014-05-10, ISBN 978-1-4832-1637-9. (באנגלית)

ieee.org

ieeexplore.ieee.org

Octavio Loyola-Gonzalez, Black-Box vs. White-Box: Understanding Their Advantages and Weaknesses From a Practical Point of View, IEEE Access 7, 2019, עמ' 154096–154113 doi: 10.1109/ACCESS.2019.2949286
Ribana Roscher, Bastian Bohn, Marco F. Duarte, Jochen Garcke, Explainable Machine Learning for Scientific Insights and Discoveries, IEEE Access 8, 2020, עמ' 42200–42216 doi: 10.1109/ACCESS.2020.2976199
Amina Adadi, Mohammed Berrada, Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI), IEEE Access 6, 2018, עמ' 52138–52160 doi: 10.1109/ACCESS.2018.2870052
Filip Karlo Dosilovic, Mario Brcic, Nikica Hlupic, Explainable artificial intelligence: A survey, IEEE, 2018-05, עמ' 0210–0215 doi: 10.23919/MIPRO.2018.8400040
A.B. Tickle, R. Andrews, M. Golea, J. Diederich, 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, Nov./1998, עמ' 1057–1068 doi: 10.1109/72.728352

ijcai.org

Kacper Sokol, Peter Flach, Glass-Box: Explaining AI Decisions With Counterfactual Statements Through Conversation With a Voice-enabled Virtual Assistant, International Joint Conferences on Artificial Intelligence Organization, 2018-07, עמ' 5868–5870 doi: 10.24963/ijcai.2018/865

mdpi.com

Giulia Vilone, Luca Longo, Classification of Explainable Artificial Intelligence Methods through Their Output Formats, Machine Learning and Knowledge Extraction 3, 2021-08-04, עמ' 615–661 doi: 10.3390/make3030032
Jose Bernal, Claudia Mazo, Transparency of Artificial Intelligence in Healthcare: Insights from Professionals in Computing and Healthcare Worldwide, Applied Sciences 12, 2022-10-11, עמ' 10228 doi: 10.3390/app122010228
Anna Markella Antoniadi, Yuhan Du, Yasmine Guendouz, Lan Wei, Claudia Mazo, Brett A. Becker, Catherine Mooney, Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review, Applied Sciences 11, 2021-05-31, עמ' 5088 doi: 10.3390/app11115088
Philip Gouverneur, Frédéric Li, Kimiaki Shirahama, Luisa Luebke, Wacław M. Adamczyk, Tibor M. Szikszay, Kerstin Luedtke, Marcin Grzegorzek, Explainable Artificial Intelligence (XAI) in Pain Research: Understanding the Role of Electrodermal Activity for Automated Pain Recognition, Sensors 23, 2023-02-09, עמ' 1959 doi: 10.3390/s23041959

medrxiv.org

Casper Wilstup, Chris Cave, Combining symbolic regression with the Cox proportional hazards model improves prediction of heart failure deaths, 2021-01-15 doi: 10.1101/2021.01.15.21249874

misq.org

University of Antwerp, David Martens, Foster Provost, New York University, Explaining Data-Driven Document Classifications, MIS Quarterly 38, 2014-01-01, עמ' 73–99 doi: 10.25300/MISQ/2014/38.1.04

mlr.press

proceedings.mlr.press

Pang Wei Koh, Thao Nguyen, Yew Siang Tang, Stephen Mussmann, Emma Pierson, Been Kim, Percy Liang, Concept Bottleneck Models, Proceedings of the 37th International Conference on Machine Learning, PMLR, 2020-11-21, עמ' 5338–5348
Carter, B., Mueller, J., Jain, S., & Gifford, D., . What made you do this? understanding black-box decisions with sufficient input subsets., The 22nd International Conference on Artificial Intelligence and Statistics, 2019, עמ' 567-576
Shrikumar, A., Greenside, P., & Kundaje, A., . Learning important features through propagating activation differences, International conference on machine learning, 2017, עמ' 3145-3153
Sundararajan, M., Taly, A., & Yan, Q., Axiomatic attribution for deep networks., International conference on machine learning, 2017, עמ' 3319-3328
Vidal, T., & Schiffer, M., Born-again tree ensembles, International conference on machine learning, 2020, עמ' 9743-9753
Yixuan Li, Jason Yosinski, Jeff Clune, Hod Lipson, John Hopcroft, Convergent Learning: Do different neural networks learn the same representations?, Proceedings of the 1st International Workshop on Feature Extraction: Modern Questions and Challenges at NIPS 2015, PMLR, 2015-12-08, עמ' 196–212
Pang Wei Koh, Percy Liang, Understanding Black-box Predictions via Influence Functions, Proceedings of the 34th International Conference on Machine Learning, PMLR, 2017-07-17, עמ' 1885–1894

nature.com

Davide Castelvecchi, Can we open the black box of AI?, Nature 538, 2016-10, עמ' 20–23 doi: 10.1038/538020a
Cynthia Rudin, Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead, Nature Machine Intelligence 1, 2019-05-13, עמ' 206–215 doi: 10.1038/s42256-019-0048-x

neurips.cc

proceedings.neurips.cc

Lundberg, S. M., & Lee, S. I., A unified approach to interpreting model predictions, . Advances in neural information processing systems 30, 2017

nist.gov

nvlpubs.nist.gov

P Jonathon Phillips, Carina A Hahn, Peter C Fontana, Amy N Yates, Kristen Greene, David A Broniatowski, Mark A Przybocki, Four principles of explainable artificial intelligence, 2021-09-29 doi: 10.6028/nist.ir.8312

plos.org

dx.plos.org

Thilo Rieg, Janek Frick, Hermann Baumgartl, Ricardo Buettner, Demonstration of the potential of white-box machine learning approaches to gain insights from cardiovascular disease electrocardiograms, PLOS ONE 15, 2020-12-17, עמ' e0243615 doi: 10.1371/journal.pone.0243615
Sebastian Bach, Alexander Binder, Grégoire Montavon, Frederick Klauschen, Klaus-Robert Müller, Wojciech Samek, On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation, PLOS ONE 10, 2015-07-10, עמ' e0130140 doi: 10.1371/journal.pone.0130140

pnas.org

W. James Murdoch, Chandan Singh, Karl Kumbier, Reza Abbasi-Asl, Bin Yu, Definitions, methods, and applications in interpretable machine learning, Proceedings of the National Academy of Sciences 116, 2019-10-29, עמ' 22071–22080 doi: 10.1073/pnas.1900654116

sagepub.com

journals.sagepub.com

Jenna Burrell, How the machine ‘thinks’: Understanding opacity in machine learning algorithms, Big Data & Society 3, 2016-06-01, עמ' 205395171562251 doi: 10.1177/2053951715622512

science.org

David Gunning, Mark Stefik, Jaesik Choi, Timothy Miller, Simone Stumpf, Guang-Zhong Yang, XAI—Explainable artificial intelligence, Science Robotics 4, 2019-12-18 doi: 10.1126/scirobotics.aay7120

springer.com

link.springer.com

József Dombi, Orsolya Csiszár, Explainable Neural Networks Based on Fuzzy Logic and Multi-criteria Decision Tools, כרך 408, Cham: Springer International Publishing, 2021, Studies in Fuzziness and Soft Computing, ISBN 978-3-030-72279-1. (באנגלית)
Berk Ustun, Cynthia Rudin, Supersparse linear integer models for optimized medical scoring systems, Machine Learning 102, 2016-03, עמ' 349–391 doi: 10.1007/s10994-015-5528-6
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