Razložljiva umetna inteligenca (Slovenian Wikipedia)

Analysis of information sources in references of the Wikipedia article "Razložljiva umetna inteligenca" in Slovenian language version.

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21doi.org

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13worldcat.org

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9nih.gov

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4harvard.edu

18^th place

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4arxiv.org

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2web.archive.org

1^st place

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1sciencedirect.com

149^th place

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1nature.com

234^th place

148^th place

1theguardian.com

12^th place

44^th place

1researchgate.net

120^th place

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1ssrn.com

703^rd place

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1city.ac.uk

low place

1deepai.org

low place

1science.org

1,160^th place

2,609^th place

1mipro-proceedings.com

low place

1bloomberg.com

99^th place

331^st place

1openai.com

1,559^th place

1,053^rd place

1darpa.mil

low place

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. januar 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 (v angleščini). 1 (5): 206–215. arXiv:1811.10154. doi:10.1038/s42256-019-0048-x. ISSN 2522-5839. PMC 9122117. PMID 35603010.
Biecek, Przemyslaw (23. junij 2018). »DALEX: explainers for complex predictive models«. Journal of Machine Learning Research. 19: 1–5. arXiv:1806.08915. Bibcode:2018arXiv180608915B.

bloomberg.com

»DeepMind Has Simple Tests That Might Prevent Elon Musk's AI Apocalypse«. Bloomberg.com (v angleščini). 11. december 2017. Pridobljeno 30. januarja 2018.

city.ac.uk

openaccess.city.ac.uk

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

darpa.mil

»Explainable Artificial Intelligence (XAI)«. DARPA. Pridobljeno 17. julija 2017.

deepai.org

»Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI«. DeepAI. 22. oktober 2019. Pridobljeno 13. januarja 2021.

doi.org

Phillips, P. Jonathon; Hahn, Carina A.; Fontana, Peter C.; Yates, Amy N.; Greene, Kristen; Broniatowski, David A.; Przybocki, Mark A. (29. september 2021). »Four Principles of Explainable Artificial Intelligence«. doi:10.6028/nist.ir.8312. {{navedi časopis}}: Sklic journal potrebuje|journal= (pomoč)
Vilone, Giulia; Longo, Luca (december 2021). »Notions of explainability and evaluation approaches for explainable artificial intelligence«. Information Fusion. 76: 89–106. doi:10.1016/j.inffus.2021.05.009.{{navedi časopis}}: Vzdrževanje CS1: samodejni prevod datuma (povezava)
Castelvecchi, Davide (6. oktober 2016). »Can we open the black box of AI?«. Nature (v angleščini). 538 (7623): 20–23. Bibcode:2016Natur.538...20C. doi:10.1038/538020a. ISSN 0028-0836. PMID 27708329.
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. 20 (1): 3–17. doi:10.1515/icom-2021-0009.
Gunning, D.; Stefik, M.; Choi, J.; Miller, T.; Stumpf, S.; Yang, G.-Z. (18. december 2019). »XAI-Explainable artificial intelligence«. Science Robotics (v angleščini). 4 (37): eaay7120. doi:10.1126/scirobotics.aay7120. ISSN 2470-9476. PMID 33137719.
Rieg, Thilo; Frick, Janek; Baumgartl, Hermann; Buettner, Ricardo (17. december 2020). »Demonstration of the potential of white-box machine learning approaches to gain insights from cardiovascular disease electrocardiograms«. PLOS ONE (v angleščini). 15 (12): e0243615. Bibcode:2020PLoSO..1543615R. doi:10.1371/journal.pone.0243615. ISSN 1932-6203. PMC 7746264. PMID 33332440.
Vilone, Giulia; Longo, Luca (2021). »Classification of Explainable Artificial Intelligence Methods through Their Output Formats«. Machine Learning and Knowledge Extraction. 3 (3): 615–661. doi:10.3390/make3030032.
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. januar 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. (Junij 2018). »The Mythos of Model Interpretability: In machine learning, the concept of interpretability is both important and slippery«. Queue (v angleščini). 16 (3): 31–57. doi:10.1145/3236386.3241340. ISSN 1542-7730.
Montavon, Grégoire; Samek, Wojciech; Müller, Klaus-Robert (1. februar 2018). »Methods for interpreting and understanding deep neural networks«. Digital Signal Processing (v angleščini). 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 (v angleščini). 1 (5): 206–215. arXiv:1811.10154. doi:10.1038/s42256-019-0048-x. ISSN 2522-5839. PMC 9122117. PMID 35603010.
Wenninger, Simon; Kaymakci, Can; Wiethe, Christian (2022). »Explainable long-term building energy consumption prediction using QLattice«. Applied Energy. Elsevier BV. 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«. American Journal of Obstetrics and Gynecology. Elsevier BV. 227 (5): 791. doi:10.1016/j.ajog.2022.06.057. ISSN 0002-9378. PMID 35779588.
Wilstup, Casper; Cave, Chris (15. januar 2021), Combining symbolic regression with the Cox proportional hazards model improves prediction of heart failure deaths, Cold Spring Harbor Laboratory, doi:10.1101/2021.01.15.21249874
Dosilovic, Filip; Brcic, Mario; Hlupic, Nikica (25. maj 2018). Explainable Artificial Intelligence: A Survey (PDF). MIPRO 2018. Opatija, Croatia. str. 210–215. doi:10.23919/MIPRO.2018.8400040. ISBN 978-953-233-095-3. Arhivirano iz prvotnega spletišča (PDF) dne 10. decembra 2018. Pridobljeno 9. decembra 2018.
Bernal, Jose; Mazo, Claudia (11. oktober 2022). »Transparency of Artificial Intelligence in Healthcare: Insights from Professionals in Computing and Healthcare Worldwide«. Applied Sciences (v angleščini). 12 (20): 10228. doi:10.3390/app122010228. ISSN 2076-3417.
Antoniadi, Anna Markella; Du, Yuhan; Guendouz, Yasmine; Wei, Lan; Mazo, Claudia; Becker, Brett A.; Mooney, Catherine (Januar 2021). »Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review«. Applied Sciences (v angleščini). 11 (11): 5088. doi:10.3390/app11115088. ISSN 2076-3417.
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. str. 5868–5870. doi:10.24963/ijcai.2018/865. ISBN 9780999241127.

harvard.edu

ui.adsabs.harvard.edu

Castelvecchi, Davide (6. oktober 2016). »Can we open the black box of AI?«. Nature (v angleščini). 538 (7623): 20–23. Bibcode:2016Natur.538...20C. doi:10.1038/538020a. ISSN 0028-0836. PMID 27708329.
Rieg, Thilo; Frick, Janek; Baumgartl, Hermann; Buettner, Ricardo (17. december 2020). »Demonstration of the potential of white-box machine learning approaches to gain insights from cardiovascular disease electrocardiograms«. PLOS ONE (v angleščini). 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. januar 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. junij 2018). »DALEX: explainers for complex predictive models«. Journal of Machine Learning Research. 19: 1–5. arXiv:1806.08915. Bibcode:2018arXiv180608915B.

mipro-proceedings.com

docs.mipro-proceedings.com

Dosilovic, Filip; Brcic, Mario; Hlupic, Nikica (25. maj 2018). Explainable Artificial Intelligence: A Survey (PDF). MIPRO 2018. Opatija, Croatia. str. 210–215. doi:10.23919/MIPRO.2018.8400040. ISBN 978-953-233-095-3. Arhivirano iz prvotnega spletišča (PDF) dne 10. decembra 2018. Pridobljeno 9. decembra 2018.

nature.com

Castelvecchi, Davide (6. oktober 2016). »Can we open the black box of AI?«. Nature (v angleščini). 538 (7623): 20–23. Bibcode:2016Natur.538...20C. doi:10.1038/538020a. ISSN 0028-0836. PMID 27708329.

nih.gov

pubmed.ncbi.nlm.nih.gov

Castelvecchi, Davide (6. oktober 2016). »Can we open the black box of AI?«. Nature (v angleščini). 538 (7623): 20–23. Bibcode:2016Natur.538...20C. doi:10.1038/538020a. ISSN 0028-0836. PMID 27708329.
Gunning, D.; Stefik, M.; Choi, J.; Miller, T.; Stumpf, S.; Yang, G.-Z. (18. december 2019). »XAI-Explainable artificial intelligence«. Science Robotics (v angleščini). 4 (37): eaay7120. doi:10.1126/scirobotics.aay7120. ISSN 2470-9476. PMID 33137719.
Rieg, Thilo; Frick, Janek; Baumgartl, Hermann; Buettner, Ricardo (17. december 2020). »Demonstration of the potential of white-box machine learning approaches to gain insights from cardiovascular disease electrocardiograms«. PLOS ONE (v angleščini). 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. januar 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 (v angleščini). 1 (5): 206–215. arXiv:1811.10154. doi:10.1038/s42256-019-0048-x. ISSN 2522-5839. PMC 9122117. PMID 35603010.
Christiansen, Michael; Wilstrup, Casper; Hedley, Paula L. (2022). »Explainable "white-box" machine learning is the way forward in preeclampsia screening«. American Journal of Obstetrics and Gynecology. Elsevier BV. 227 (5): 791. doi:10.1016/j.ajog.2022.06.057. ISSN 0002-9378. PMID 35779588.

ncbi.nlm.nih.gov

Rieg, Thilo; Frick, Janek; Baumgartl, Hermann; Buettner, Ricardo (17. december 2020). »Demonstration of the potential of white-box machine learning approaches to gain insights from cardiovascular disease electrocardiograms«. PLOS ONE (v angleščini). 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. januar 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 (v angleščini). 1 (5): 206–215. arXiv:1811.10154. doi:10.1038/s42256-019-0048-x. ISSN 2522-5839. PMC 9122117. PMID 35603010.

openai.com

blog.openai.com

»Learning from Human Preferences«. OpenAI Blog. 13. junij 2017. Arhivirano iz prvotnega spletišča dne 28. marca 2023. Pridobljeno 30. januarja 2018.

researchgate.net

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. 20 (1): 3–17. doi:10.1515/icom-2021-0009.

science.org

»How AI detectives are cracking open the black box of deep learning«. Science (v angleščini). 5. julij 2017. Pridobljeno 30. januarja 2018..

sciencedirect.com

Vilone, Giulia; Longo, Luca (december 2021). »Notions of explainability and evaluation approaches for explainable artificial intelligence«. Information Fusion. 76: 89–106. doi:10.1016/j.inffus.2021.05.009.{{navedi časopis}}: Vzdrževanje CS1: samodejni prevod datuma (povezava)

ssrn.com

papers.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. SSRN 2972855.

theguardian.com

Sample, Ian (5. november 2017). »Computer says no: why making AIs fair, accountable and transparent is crucial«. The Guardian (v angleščini). Pridobljeno 30. januarja 2018.

web.archive.org

Dosilovic, Filip; Brcic, Mario; Hlupic, Nikica (25. maj 2018). Explainable Artificial Intelligence: A Survey (PDF). MIPRO 2018. Opatija, Croatia. str. 210–215. doi:10.23919/MIPRO.2018.8400040. ISBN 978-953-233-095-3. Arhivirano iz prvotnega spletišča (PDF) dne 10. decembra 2018. Pridobljeno 9. decembra 2018.
»Learning from Human Preferences«. OpenAI Blog. 13. junij 2017. Arhivirano iz prvotnega spletišča dne 28. marca 2023. Pridobljeno 30. januarja 2018.

worldcat.org

Castelvecchi, Davide (6. oktober 2016). »Can we open the black box of AI?«. Nature (v angleščini). 538 (7623): 20–23. Bibcode:2016Natur.538...20C. doi:10.1038/538020a. ISSN 0028-0836. PMID 27708329.
Gunning, D.; Stefik, M.; Choi, J.; Miller, T.; Stumpf, S.; Yang, G.-Z. (18. december 2019). »XAI-Explainable artificial intelligence«. Science Robotics (v angleščini). 4 (37): eaay7120. doi:10.1126/scirobotics.aay7120. ISSN 2470-9476. PMID 33137719.
Rieg, Thilo; Frick, Janek; Baumgartl, Hermann; Buettner, Ricardo (17. december 2020). »Demonstration of the potential of white-box machine learning approaches to gain insights from cardiovascular disease electrocardiograms«. PLOS ONE (v angleščini). 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.
Lipton, Zachary C. (Junij 2018). »The Mythos of Model Interpretability: In machine learning, the concept of interpretability is both important and slippery«. Queue (v angleščini). 16 (3): 31–57. doi:10.1145/3236386.3241340. ISSN 1542-7730.
Montavon, Grégoire; Samek, Wojciech; Müller, Klaus-Robert (1. februar 2018). »Methods for interpreting and understanding deep neural networks«. Digital Signal Processing (v angleščini). 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 (v angleščini). 1 (5): 206–215. arXiv:1811.10154. doi:10.1038/s42256-019-0048-x. ISSN 2522-5839. PMC 9122117. PMID 35603010.
Wenninger, Simon; Kaymakci, Can; Wiethe, Christian (2022). »Explainable long-term building energy consumption prediction using QLattice«. Applied Energy. Elsevier BV. 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«. American Journal of Obstetrics and Gynecology. Elsevier BV. 227 (5): 791. doi:10.1016/j.ajog.2022.06.057. ISSN 0002-9378. PMID 35779588.
Bernal, Jose; Mazo, Claudia (11. oktober 2022). »Transparency of Artificial Intelligence in Healthcare: Insights from Professionals in Computing and Healthcare Worldwide«. Applied Sciences (v angleščini). 12 (20): 10228. doi:10.3390/app122010228. ISSN 2076-3417.
Antoniadi, Anna Markella; Du, Yuhan; Guendouz, Yasmine; Wei, Lan; Mazo, Claudia; Becker, Brett A.; Mooney, Catherine (Januar 2021). »Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review«. Applied Sciences (v angleščini). 11 (11): 5088. doi:10.3390/app11115088. ISSN 2076-3417.