ذكاء اصطناعي قابل للتفسير (Arabic Wikipedia)

Analysis of information sources in references of the Wikipedia article "ذكاء اصطناعي قابل للتفسير" in Arabic language version.

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43web.archive.org

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acm.org

dl.acm.org

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Bhatt، Umang؛ Xiang، Alice؛ Sharma، Shubham؛ Weller، Adrian؛ Taly، Ankur؛ Jia، Yunhan؛ Ghosh، Joydeep؛ Puri، Richir؛ M.F. Moura، José (2022). "Explainable Machine Learning in Deployment". Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency. Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency. ص. 648–657. DOI:10.1145/3351095.3375624. ISBN:9781450369367.
Veale، Michael؛ Van Kleek، Max؛ Binns، Reuben (2018). "Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making". Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. ج. 40. ص. 1–14. DOI:10.1145/3173574.3174014. ISBN:9781450356206.
Cheng، Hao-Fei؛ Wang، Ruotang؛ Zhang، Zheng؛ O’Connell، Fiona؛ Gray، Terrance؛ Harper، F. Maxwell؛ Zhu، Haiyi (2019). Explaining Decision-Making Algorithms through UI: Strategies to Help Non-Expert Stakeholders. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. ج. 559. ص. 1–12. DOI:10.1145/3290605.3300789. ISBN:9781450359702. مؤرشف من الأصل في 2023-02-10.
Cheng، Hao-Fei؛ Wang، Ruotang؛ Zhang، Zheng؛ O’Connell، Fiona؛ Gray، Terrance؛ Harper، F. Maxwell؛ Zhu، Haiyi (2019). Explaining Decision-Making Algorithms through UI: Strategies to Help Non-Expert Stakeholders. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. ج. 559. ص. 1–12. DOI:10.1145/3290605.3300789. ISBN:9781450359702. مؤرشف من الأصل في 2023-02-10.

archive.org

Clancey، William (1987). Knowledge-Based Tutoring: The GUIDON Program. Cambridge, Massachusetts: The MIT Press.
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arxiv.org

Roscher، R.؛ Bohn، B.؛ Duarte، M. F.؛ Garcke، J. (2020). "Explainable Machine Learning for Scientific Insights and Discoveries". IEEE Access. ج. 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 يناير 2019). "Interpretable machine learning: definitions, methods, and applications". Proceedings of the National Academy of Sciences of the United States of America. ج. 116 ع. 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 (بالإنجليزية). 1 (5): 206–215. arXiv:1811.10154. DOI:10.1038/s42256-019-0048-x. ISSN:2522-5839. PMC:9122117. PMID:35603010.
Biecek، Przemyslaw (23 يونيو 2018). "DALEX: explainers for complex predictive models". Journal of Machine Learning Research. ج. 19: 1–5. arXiv:1806.08915. Bibcode:2018arXiv180608915B.
Csiszár, Orsolya; Csiszár, Gábor; Dombi, József (8 Jul 2020). "Interpretable neural networks based on continuous-valued logic and multicriteria decision operators". Knowledge-Based Systems (بالإنجليزية). 199: 105972. arXiv:1910.02486. DOI:10.1016/j.knosys.2020.105972. ISSN:0950-7051.
Lundberg، Scott M؛ Lee، Su-In (2017)، Guyon؛ Luxburg، U. V.؛ Bengio، S.؛ Wallach، H. (المحررون)، "A Unified Approach to Interpreting Model Predictions" (PDF)، Advances in Neural Information Processing Systems 30، Curran Associates, Inc.، ص. 4765–4774، arXiv:1705.07874، Bibcode:2017arXiv170507874L، مؤرشف من الأصل (PDF) في 2023-03-20، اطلع عليه بتاريخ 2020-03-13
Aivodji, Ulrich; Arai, Hiromi; Fortineau, Olivier; Gambs, Sébastien; Hara, Satoshi; Tapp, Alain (24 May 2019). "Fairwashing: the risk of rationalization". PMLR (بالإنجليزية): 161–170. arXiv:1901.09749. Archived from the original on 2022-08-09.
Le Merrer, Erwan; Trédan, Gilles (Sep 2020). "Remote explainability faces the bouncer problem". Nature Machine Intelligence (بالإنجليزية). 2 (9): 529–539. arXiv:1910.01432. DOI:10.1038/s42256-020-0216-z. ISSN:2522-5839. Archived from the original on 2023-02-14.
Vidal, Thibaut; Schiffer, Maximilian (2020). "Born-Again Tree Ensembles". PMLR (بالإنجليزية). 119: 9743–9753. arXiv:2003.11132. Archived from the original on 2023-03-06.
Hendricks, Lisa Anne; Akata, Zeynep; Rohrbach, Marcus; Donahue, Jeff; Schiele, Bernt; Darrell, Trevor (2016). "Generating Visual Explanations". Springer International Publishing. Lecture Notes in Computer Science (بالإنجليزية). 9908: 3–19. arXiv:1603.08507. DOI:10.1007/978-3-319-46493-0_1. ISBN:978-3-319-46492-3. Archived from the original on 2023-03-16.
Koh, Pang Wei; Liang, Percy (17 Jul 2017). "Understanding Black-box Predictions via Influence Functions". PMLR (بالإنجليزية): 1885–1894. arXiv:1703.04730. Archived from the original on 2022-07-12.
Sarkar، Advait (2022). "Is explainable AI a race against model complexity?" (PDF). Workshop on Transparency and Explanations in Smart Systems (TeXSS), in Conjunction with ACM Intelligent User Interfaces (IUI 2022): 192–199. arXiv:2205.10119. مؤرشف من الأصل (PDF) في 2023-01-16 – عبر CEUR Workshop Proceedings.

bloomberg.com

"DeepMind Has Simple Tests That Might Prevent Elon Musk's AI Apocalypse". Bloomberg.com (بالإنجليزية). 11 Dec 2017. Archived from the original on 2022-11-28. Retrieved 2018-01-30.
"Accenture Unveils Tool to Help Companies Insure Their AI Is Fair". Bloomberg.com (بالإنجليزية). Jun 2018. Archived from the original on 2022-12-03. Retrieved 2018-08-05.
Kahn، Jeremy (12 ديسمبر 2018). "Artificial Intelligence Has Some Explaining to Do". بلومبيرغ بيزنس ويك. مؤرشف من الأصل في 2022-07-23. اطلع عليه بتاريخ 2018-12-17.

ceur-ws.org

Sarkar، Advait (2022). "Is explainable AI a race against model complexity?" (PDF). Workshop on Transparency and Explanations in Smart Systems (TeXSS), in Conjunction with ACM Intelligent User Interfaces (IUI 2022): 192–199. arXiv:2205.10119. مؤرشف من الأصل (PDF) في 2023-01-16 – عبر CEUR Workshop Proceedings.

city.ac.uk

openaccess.city.ac.uk

Gunning, D.; Stefik, M.; Choi, J.; Miller, T.; Stumpf, S.; Yang, G.-Z. (18 Dec 2019). "XAI-Explainable artificial intelligence". Science Robotics (بالإنجليزية). 4 (37): eaay7120. DOI:10.1126/scirobotics.aay7120. ISSN:2470-9476. PMID:33137719. Archived from the original on 2022-03-15.
Gunning, D.; Stefik, M.; Choi, J.; Miller, T.; Stumpf, S.; Yang, G.-Z. (18 Dec 2019). "XAI-Explainable artificial intelligence". Science Robotics (بالإنجليزية). 4 (37): eaay7120. DOI:10.1126/scirobotics.aay7120. ISSN:2470-9476. PMID:33137719. Archived from the original on 2022-03-15.

darpa.mil

"Explainable Artificial Intelligence (XAI)". DARPA. DARPA. مؤرشف من الأصل في 2022-12-17. اطلع عليه بتاريخ 2017-07-17.

deepai.org

"Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI". DeepAI. 22 أكتوبر 2019. مؤرشف من الأصل في 2022-01-20. اطلع عليه بتاريخ 2021-01-13.

distill.pub

Olah, Chris; Cammarata, Nick; Schubert, Ludwig; Goh, Gabriel; Petrov, Michael; Carter, Shan (10 Mar 2020). "Zoom In: An Introduction to Circuits". Distill (بالإنجليزية). 5 (3): e00024.001. DOI:10.23915/distill.00024.001. ISSN:2476-0757. Archived from the original on 2023-03-30.

doi.org

Phillips، P. Jonathon؛ Hahn، Carina A.؛ Fontana، Peter C.؛ Yates، Amy N.؛ Greene، Kristen؛ Broniatowski، David A.؛ Przybocki، Mark A. (29 سبتمبر 2021). "Four Principles of Explainable Artificial Intelligence". DOI:10.6028/nist.ir.8312. مؤرشف من الأصل في 2023-02-14. {{استشهاد بدورية محكمة}}: الاستشهاد بدورية محكمة يطلب |دورية محكمة= (مساعدة)
Vilone، Giulia؛ Longo، Luca (2021). "Notions of explainability and evaluation approaches for explainable artificial intelligence". Information Fusion. December 2021 - Volume 76: 89–106. DOI:10.1016/j.inffus.2021.05.009. مؤرشف من الأصل في 2023-02-14.
Castelvecchi, Davide (6 Oct 2016). "Can we open the black box of AI?". Nature (بالإنجليزية). 538 (7623): 20–23. Bibcode:2016Natur.538...20C. DOI:10.1038/538020a. ISSN:0028-0836. PMID:27708329. Archived from the original on 2023-03-21.
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. ج. 20 ع. 1: 3–17. DOI:10.1515/icom-2021-0009. مؤرشف من الأصل في 2022-05-20.
Gunning, D.; Stefik, M.; Choi, J.; Miller, T.; Stumpf, S.; Yang, G.-Z. (18 Dec 2019). "XAI-Explainable artificial intelligence". Science Robotics (بالإنجليزية). 4 (37): eaay7120. DOI:10.1126/scirobotics.aay7120. ISSN:2470-9476. PMID:33137719. Archived from the original on 2022-03-15.
Rieg, Thilo; Frick, Janek; Baumgartl, Hermann; Buettner, Ricardo (17 Dec 2020). "Demonstration of the potential of white-box machine learning approaches to gain insights from cardiovascular disease electrocardiograms". PLOS ONE (بالإنجليزية). 15 (12): e0243615. Bibcode:2020PLoSO..1543615R. DOI:10.1371/journal.pone.0243615. ISSN:1932-6203. PMC:7746264. PMID:33332440.{{استشهاد بدورية محكمة}}: صيانة الاستشهاد: دوي مجاني غير معلم (link)
Vilone، Giulia؛ Longo، Luca (2021). "Classification of Explainable Artificial Intelligence Methods through Their Output Formats". Machine Learning and Knowledge Extraction. ج. 3 ع. 3: 615–661. DOI:10.3390/make3030032.{{استشهاد بدورية محكمة}}: صيانة الاستشهاد: دوي مجاني غير معلم (link)
Loyola-González، O. (2019). "Black-Box vs. White-Box: Understanding Their Advantages and Weaknesses From a Practical Point of View". IEEE Access. ج. 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. ج. 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 يناير 2019). "Interpretable machine learning: definitions, methods, and applications". Proceedings of the National Academy of Sciences of the United States of America. ج. 116 ع. 44: 22071–22080. arXiv:1901.04592. Bibcode:2019arXiv190104592M. DOI:10.1073/pnas.1900654116. PMC:6825274. PMID:31619572.
Lipton, Zachary C. (Jun 2018). "The Mythos of Model Interpretability: In machine learning, the concept of interpretability is both important and slippery". Queue (بالإنجليزية). 16 (3): 31–57. DOI:10.1145/3236386.3241340. ISSN:1542-7730.
Montavon, Grégoire; Samek, Wojciech; Müller, Klaus-Robert (1 Feb 2018). "Methods for interpreting and understanding deep neural networks". Digital Signal Processing (بالإنجليزية). 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. ج. 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 (بالإنجليزية). 1 (5): 206–215. arXiv:1811.10154. DOI:10.1038/s42256-019-0048-x. ISSN:2522-5839. PMC:9122117. PMID:35603010.
Gunning, D.; Stefik, M.; Choi, J.; Miller, T.; Stumpf, S.; Yang, G.-Z. (18 Dec 2019). "XAI-Explainable artificial intelligence". Science Robotics (بالإنجليزية). 4 (37): eaay7120. DOI:10.1126/scirobotics.aay7120. ISSN:2470-9476. PMID:33137719. Archived from the original on 2022-03-15.
Wenninger، Simon؛ Kaymakci، Can؛ Wiethe، Christian (2022). "Explainable long-term building energy consumption prediction using QLattice". Elsevier BV. ج. 308: 118300. DOI:10.1016/j.apenergy.2021.118300. ISSN:0306-2619.
Christiansen، Michael؛ Wilstrup، Casper؛ Hedley، Paula L. (2022). "Explainable "white-box" machine learning is the way forward in preeclampsia screening". Elsevier BV. ج. 227 ع. 5: 791. DOI:10.1016/j.ajog.2022.06.057. ISSN:0002-9378. PMID:35779588.
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Antoniadi, Anna Markella; Du, Yuhan; Guendouz, Yasmine; Wei, Lan; Mazo, Claudia; Becker, Brett A.; Mooney, Catherine (Jan 2021). "Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review". Applied Sciences (بالإنجليزية). 11 (11): 5088. DOI:10.3390/app11115088. ISSN:2076-3417.{{استشهاد بدورية محكمة}}: صيانة الاستشهاد: دوي مجاني غير معلم (link)
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Csiszár, Orsolya; Csiszár, Gábor; Dombi, József (8 Jul 2020). "Interpretable neural networks based on continuous-valued logic and multicriteria decision operators". Knowledge-Based Systems (بالإنجليزية). 199: 105972. arXiv:1910.02486. DOI:10.1016/j.knosys.2020.105972. ISSN:0950-7051.
Dombi, József; Csiszár, Orsolya (2021). "Explainable Neural Networks Based on Fuzzy Logic and Multi-criteria Decision Tools". Studies in Fuzziness and Soft Computing (بالإنجليزية البريطانية). 408. DOI:10.1007/978-3-030-72280-7. ISBN:978-3-030-72279-1. ISSN:1434-9922. Archived from the original on 2023-02-14.
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""Why Should I Trust You?" | Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining" (بالإنجليزية). DOI:10.1145/2939672.2939778. {{استشهاد بدورية محكمة}}: الاستشهاد بدورية محكمة يطلب |دورية محكمة= (help)
Le Merrer, Erwan; Trédan, Gilles (Sep 2020). "Remote explainability faces the bouncer problem". Nature Machine Intelligence (بالإنجليزية). 2 (9): 529–539. arXiv:1910.01432. DOI:10.1038/s42256-020-0216-z. ISSN:2522-5839. Archived from the original on 2023-02-14.
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Olah, Chris; Cammarata, Nick; Schubert, Ludwig; Goh, Gabriel; Petrov, Michael; Carter, Shan (10 Mar 2020). "Zoom In: An Introduction to Circuits". Distill (بالإنجليزية). 5 (3): e00024.001. DOI:10.23915/distill.00024.001. ISSN:2476-0757. Archived from the original on 2023-03-30.
Hendricks, Lisa Anne; Akata, Zeynep; Rohrbach, Marcus; Donahue, Jeff; Schiele, Bernt; Darrell, Trevor (2016). "Generating Visual Explanations". Springer International Publishing. Lecture Notes in Computer Science (بالإنجليزية). 9908: 3–19. arXiv:1603.08507. DOI:10.1007/978-3-319-46493-0_1. ISBN:978-3-319-46492-3. Archived from the original on 2023-03-16.
Bhatt، Umang؛ Xiang، Alice؛ Sharma، Shubham؛ Weller، Adrian؛ Taly، Ankur؛ Jia، Yunhan؛ Ghosh، Joydeep؛ Puri، Richir؛ M.F. Moura، José (2022). "Explainable Machine Learning in Deployment". Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency. Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency. ص. 648–657. DOI:10.1145/3351095.3375624. ISBN:9781450369367.
Burrel، Jenna (2016). "How the machine 'thinks': Understanding opacity in machine learning algorithms". Big Data & Society. ج. 3 ع. 1. DOI:10.1177/2053951715622512. مؤرشف من الأصل في 2022-12-26. {{استشهاد بدورية محكمة}}: الاستشهاد بدورية محكمة يطلب |دورية محكمة= (مساعدة)
Veale، Michael؛ Van Kleek، Max؛ Binns، Reuben (2018). "Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making". Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. ج. 40. ص. 1–14. DOI:10.1145/3173574.3174014. ISBN:9781450356206.
Burrel، Jenna (2016). "How the machine 'thinks': Understanding opacity in machine learning algorithms". Big Data & Society. ج. 3 ع. 1. DOI:10.1177/2053951715622512. مؤرشف من الأصل في 2022-12-26. {{استشهاد بدورية محكمة}}: الاستشهاد بدورية محكمة يطلب |دورية محكمة= (مساعدة)
Cheng، Hao-Fei؛ Wang، Ruotang؛ Zhang، Zheng؛ O’Connell، Fiona؛ Gray، Terrance؛ Harper، F. Maxwell؛ Zhu، Haiyi (2019). Explaining Decision-Making Algorithms through UI: Strategies to Help Non-Expert Stakeholders. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. ج. 559. ص. 1–12. DOI:10.1145/3290605.3300789. ISBN:9781450359702. مؤرشف من الأصل في 2023-02-10.
Cheng، Hao-Fei؛ Wang، Ruotang؛ Zhang، Zheng؛ O’Connell، Fiona؛ Gray، Terrance؛ Harper، F. Maxwell؛ Zhu، Haiyi (2019). Explaining Decision-Making Algorithms through UI: Strategies to Help Non-Expert Stakeholders. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. ج. 559. ص. 1–12. DOI:10.1145/3290605.3300789. ISBN:9781450359702. مؤرشف من الأصل في 2023-02-10.
McCoy, Liam G.; Brenna, Connor T. A.; Chen, Stacy S.; Vold, Karina; Das, Sunit (5 Nov 2021). "Believing in black boxes: machine learning for healthcare does not need explainability to be evidence-based". Journal of Clinical Epidemiology (بالإنجليزية). 142 (Online ahead of print): 252–257. DOI:10.1016/j.jclinepi.2021.11.001. ISSN:0895-4356. PMID:34748907. Archived from the original on 2021-12-15.
Ghassemi, Marzyeh; Oakden-Rayner, Luke; Beam, Andrew L. (1 Nov 2021). "The false hope of current approaches to explainable artificial intelligence in health care". The Lancet Digital Health (بالإنجليزية). 3 (11): e745–e750. DOI:10.1016/S2589-7500(21)00208-9. ISSN:2589-7500. PMID:34711379. Archived from the original on 2021-12-15.

economist.com

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معجم البيانات والذكاء الاصطناعي (PDF) (بالعربية والإنجليزية)، الهيئة السعودية للبيانات والذكاء الاصطناعي، 2022، ص. 67، QID:Q111421033

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