Megmagyarázható mesterséges intelligencia (Hungarian Wikipedia)

Analysis of information sources in references of the Wikipedia article "Megmagyarázható mesterséges intelligencia" in Hungarian language version.

refsWebsite

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14doi.org

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5worldcat.org

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3nih.gov

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3acm.org

1,185^th place

2,600^th place

2sciencedirect.com

149^th place

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2bloomberg.com

99^th place

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2web.archive.org

1^st place

1ercim.eu

low place

1nature.com

234^th place

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1theguardian.com

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1openai.com

1,559^th place

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1darpa.mil

low place

1intelligentrobots.org

low place

1sagepub.com

731^st place

2,041^st place

1jclinepi.com

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1thelancet.com

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8,307^th place

1ceur-ws.org

low place

acm.org

dl.acm.org

Bhatt, Umang. Explainable Machine Learning in Deployment, Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 648–657. o.. DOI: 10.1145/3351095.3375624 (2022. december 21.). ISBN 9781450369367
Veale, Michael. 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, 1–14. o.. DOI: 10.1145/3173574.3174014 (2018. december 21.). ISBN 9781450356206
Cheng, Hao-Fei. Explaining Decision-Making Algorithms through UI: Strategies to Help Non-Expert Stakeholders, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, 1–12. o.. DOI: 10.1145/3290605.3300789 (2019. december 21.). ISBN 9781450359702

bloomberg.com

„DeepMind Has Simple Tests That Might Prevent Elon Musk's AI Apocalypse”, Bloomberg.com, 2017. december 11. (Hozzáférés: 2018. január 30.) (angol nyelvű)
Kahn. „Artificial Intelligence Has Some Explaining to Do”, Bloomberg Businessweek, 2018. december 12. (Hozzáférés: 2018. december 17.)

ceur-ws.org

Sarkar (2022. december 21.). „Is explainable AI a race against model complexity?”. Workshop on Transparency and Explanations in Smart Systems (TeXSS), in Conjunction with ACM Intelligent User Interfaces (IUI 2022), 192–199. o.

darpa.mil

Explainable Artificial Intelligence (XAI). DARPA. (Hozzáférés: 2017. július 17.)

doi.org

dx.doi.org

Phillips (2021. szeptember 29.). „Four Principles of Explainable Artificial Intelligence”. DOI:10.6028/nist.ir.8312.
Vilone (2021). „Notions of explainability and evaluation approaches for explainable artificial intelligence”. Information Fusion December 2021 - Volume 76, 89–106. o. DOI:10.1016/j.inffus.2021.05.009.
Castelvecchi (2016. október 6.). „Can we open the black box of AI?” (angol nyelven). Nature 538 (7623), 20–23. o. DOI:10.1038/538020a. ISSN 0028-0836. PMID 27708329.
Bernal (2022. október 11.). „Transparency of Artificial Intelligence in Healthcare: Insights from Professionals in Computing and Healthcare Worldwide” (angol nyelven). Applied Sciences 12 (20), 10228. o. DOI:10.3390/app122010228. ISSN 2076-3417. (Hozzáférés: Hiba: Érvénytelen idő.)
Antoniadi (2021. január 1.). „Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review” (angol nyelven). Applied Sciences 11 (11), 5088. o. DOI:10.3390/app11115088. ISSN 2076-3417. (Hozzáférés: Hiba: Érvénytelen idő.)
Sokol, Kacper. 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, 5868–5870. o.. DOI: 10.24963/ijcai.2018/865 (2018). ISBN 9780999241127
Bhatt, Umang. Explainable Machine Learning in Deployment, Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 648–657. o.. DOI: 10.1145/3351095.3375624 (2022. december 21.). ISBN 9781450369367
Burrel (2016. december 21.). „How the machine 'thinks': Understanding opacity in machine learning algorithms” 3 (1). DOI:10.1177/2053951715622512.
Veale, Michael. 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, 1–14. o.. DOI: 10.1145/3173574.3174014 (2018. december 21.). ISBN 9781450356206
Hu (2023. december 21.). „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, 109458. o. DOI:10.1016/j.agrformet.2023.109458.
Cheng, Hao-Fei. Explaining Decision-Making Algorithms through UI: Strategies to Help Non-Expert Stakeholders, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, 1–12. o.. DOI: 10.1145/3290605.3300789 (2019. december 21.). ISBN 9781450359702
McCoy (2021. november 5.). „Believing in black boxes: machine learning for healthcare does not need explainability to be evidence-based” (english nyelven). Journal of Clinical Epidemiology 142 (Online ahead of print), 252–257. o. DOI:10.1016/j.jclinepi.2021.11.001. ISSN 0895-4356. PMID 34748907.
Ghassemi (2021. november 1.). „The false hope of current approaches to explainable artificial intelligence in health care” (english nyelven). The Lancet Digital Health 3 (11), e745–e750. o. DOI:10.1016/S2589-7500(21)00208-9. ISSN 2589-7500. PMID 34711379.

doi.org

Phillips (2021. szeptember 29.). „Four Principles of Explainable Artificial Intelligence”. DOI:10.6028/nist.ir.8312.

ercim.eu

ercim-news.ercim.eu

Mihály (2023. december 21.). „Explainable AI: A Brief History of the Concept”. ERCIM News (134), 9–10. o.

intelligentrobots.org

IJCAI 2017 Workshop on Explainable Artificial Intelligence (XAI). Earthlink. IJCAI. [2019. április 4-i dátummal az eredetiből archiválva]. (Hozzáférés: 2017. július 17.)

jclinepi.com

McCoy (2021. november 5.). „Believing in black boxes: machine learning for healthcare does not need explainability to be evidence-based” (english nyelven). Journal of Clinical Epidemiology 142 (Online ahead of print), 252–257. o. DOI:10.1016/j.jclinepi.2021.11.001. ISSN 0895-4356. PMID 34748907.

nature.com

Castelvecchi (2016. október 6.). „Can we open the black box of AI?” (angol nyelven). Nature 538 (7623), 20–23. o. DOI:10.1038/538020a. ISSN 0028-0836. PMID 27708329.

nih.gov

pubmed.ncbi.nlm.nih.gov

Castelvecchi (2016. október 6.). „Can we open the black box of AI?” (angol nyelven). Nature 538 (7623), 20–23. o. DOI:10.1038/538020a. ISSN 0028-0836. PMID 27708329.
McCoy (2021. november 5.). „Believing in black boxes: machine learning for healthcare does not need explainability to be evidence-based” (english nyelven). Journal of Clinical Epidemiology 142 (Online ahead of print), 252–257. o. DOI:10.1016/j.jclinepi.2021.11.001. ISSN 0895-4356. PMID 34748907.
Ghassemi (2021. november 1.). „The false hope of current approaches to explainable artificial intelligence in health care” (english nyelven). The Lancet Digital Health 3 (11), e745–e750. o. DOI:10.1016/S2589-7500(21)00208-9. ISSN 2589-7500. PMID 34711379.

openai.com

blog.openai.com

„Learning from Human Preferences”, OpenAI Blog, 2017. június 13.. [2023. március 28-i dátummal az eredetiből archiválva] (Hozzáférés: 2018. január 30.)

sagepub.com

journals.sagepub.com

Burrel (2016. december 21.). „How the machine 'thinks': Understanding opacity in machine learning algorithms” 3 (1). DOI:10.1177/2053951715622512.

sciencedirect.com

Vilone (2021). „Notions of explainability and evaluation approaches for explainable artificial intelligence”. Information Fusion December 2021 - Volume 76, 89–106. o. DOI:10.1016/j.inffus.2021.05.009.
Hu (2023. december 21.). „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, 109458. o. DOI:10.1016/j.agrformet.2023.109458.

theguardian.com

Sample. „Computer says no: why making AIs fair, accountable and transparent is crucial”, The Guardian, 2017. november 5. (Hozzáférés: 2018. január 30.) (angol nyelvű)

thelancet.com

Ghassemi (2021. november 1.). „The false hope of current approaches to explainable artificial intelligence in health care” (english nyelven). The Lancet Digital Health 3 (11), e745–e750. o. DOI:10.1016/S2589-7500(21)00208-9. ISSN 2589-7500. PMID 34711379.

web.archive.org

„Learning from Human Preferences”, OpenAI Blog, 2017. június 13.. [2023. március 28-i dátummal az eredetiből archiválva] (Hozzáférés: 2018. január 30.)
IJCAI 2017 Workshop on Explainable Artificial Intelligence (XAI). Earthlink. IJCAI. [2019. április 4-i dátummal az eredetiből archiválva]. (Hozzáférés: 2017. július 17.)

worldcat.org

Castelvecchi (2016. október 6.). „Can we open the black box of AI?” (angol nyelven). Nature 538 (7623), 20–23. o. DOI:10.1038/538020a. ISSN 0028-0836. PMID 27708329.
Bernal (2022. október 11.). „Transparency of Artificial Intelligence in Healthcare: Insights from Professionals in Computing and Healthcare Worldwide” (angol nyelven). Applied Sciences 12 (20), 10228. o. DOI:10.3390/app122010228. ISSN 2076-3417. (Hozzáférés: Hiba: Érvénytelen idő.)
Antoniadi (2021. január 1.). „Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review” (angol nyelven). Applied Sciences 11 (11), 5088. o. DOI:10.3390/app11115088. ISSN 2076-3417. (Hozzáférés: Hiba: Érvénytelen idő.)
McCoy (2021. november 5.). „Believing in black boxes: machine learning for healthcare does not need explainability to be evidence-based” (english nyelven). Journal of Clinical Epidemiology 142 (Online ahead of print), 252–257. o. DOI:10.1016/j.jclinepi.2021.11.001. ISSN 0895-4356. PMID 34748907.
Ghassemi (2021. november 1.). „The false hope of current approaches to explainable artificial intelligence in health care” (english nyelven). The Lancet Digital Health 3 (11), e745–e750. o. DOI:10.1016/S2589-7500(21)00208-9. ISSN 2589-7500. PMID 34711379.