Охота на электроовец. Большая книга искусственного интеллекта - Сергей Сергеевич Марков

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CAHAI (2020). Feasibility Study // https://rm.coe.int/cahai-2020-23-final-eng-feasibility-study-/1680a0c6da

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CAHAI (2021). Possible elements of a legal framework on artificial intelligence, based on the Council of Europe’s standards on human rights, democracy and the rule of law // https://rm.coe.int/cahai-2021-09rev-elements/1680a6d90d

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CAI (2023). Revised zero draft [framework] convention on artificial intelligence, human rights, democracy and the rule of law // https://rm.coe.int/cai-2023-01-revised-zero-draft-framework-convention-public/1680aa193f

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Beazley D. (2023). Canada sits on the fence about regulating AI. / CBA/ABC National, 31 May 2023 // https://nationalmagazine.ca/en-ca/articles/law/hot-topics-in-law/2023/canada-sits-on-the-fence-in-regulating-ai

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Bordoloi P. (2023). India Backs Off on AI Regulation. But Why? / Analytics India Magazine, April 10, 2023 // https://analyticsindiamag.com/india-backs-off-on-ai-regulation-but-why/

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For the first time in Israel: The principles of the policy for the responsible development of the field of artificial intelligence were published for public comment (2022). / Ministry of Innovation, Science and Technology, 17.11.2022 // https://www.gov.il/en/departments/news/most-news20221117

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Ravia H., Kaplan T., Hammer D. (2021). Use of Artificial Intelligence Attracts Legislative and Regulatory Attention in the E.U., U.S., and Israel. / Pearl Cohen, Apr 29, 2021 // https://www.pearlcohen.com/use-of-artificial-intelligence-attracts-legislative-and-regulatory-attention-in-the-e-u-u-s-and-israel/

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Roh T., Nam J. E. (2023). South Korea: Legislation on Artificial Intelligence to Make Significant Progress. / Kim & Chang, 2023.03.06 // https://www.kimchang.com/en/insights/detail.kc?sch_section=4&idx=26935

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Указ Президента Российской Федерации «О развитии искусственного интеллекта в Российской Федерации» (2019) // http://static.kremlin.ru/media/events/files/ru/AH4x6HgKWANwVtMOfPDhcbRpvd1HCCsv.pdf

3238

Proposed Regulatory Framework for Modifications to Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD) - Discussion Paper and Request for Feedback (2019). / U. S. Food & Drug Administration // https://www.fda.gov/files/medical%20devices/published/US-FDA-Artificial-Intelligence-and-Machine-Learning-Discussion-Paper.pdf

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Marketing Submission Recommendations for a Predetermined Change Control Plan for Artificial Intelligence/Machine Learning (AI/ML)-Enabled Device Software Functions. Draft Guidance for Industry and Food and Drug Administration Staff (2023). / U. S. Food & Drug Administration, April 2023 // https://www.fda.gov/regulatory-information/search-fda-guidance-documents/marketing-submission-recommendations-predetermined-change-control-plan-artificial

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Boubker J., Faget K. Y., Beaver N. A., Chmielewski M. R. (2023). FDA’s New Guidance Proposes Flexible Use of AI in Medical Devices / Foley, 10 May 2023 // https://www.foley.com/en/insights/publications/2023/05/fdas-guidance-flexible-use-ai-medical-devices

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Three Guidelines Published Today, Propelling China to be World Leader in Digital Health (2022). / China Med Device, March 9, 2022 // https://chinameddevice.com/digital-health-nmpa-ai/

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ISO/IEC JTC 1/SC 42 Artificial intelligence (2017) // https://www.iso.org/ru/committee/6794475.html

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Гасиоровски-Денис Е. (2020). Навстречу искусственному интеллекту // https://www.iso.org/ru/news/ref2530.html

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ISO/IEC JTC 1/SC 42 Artificial intelligence (2020). ISO/IEC TR 24028:2020. Information technology — Artificial intelligence — Overview of trustworthiness in artificial intelligence // https://www.iso.org/ru/standard/77608.html

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Представлены 36 проектов национальных стандартов в области ИИ (2021). / D-russia.ru, 18.10.2021 // https://d-russia.ru/predstavleny-36-proektov-nacionalnyh-standartov-v-oblasti-ii.html

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Федеральный закон «О персональных данных» от 27.07.2006 №152-ФЗ (2023) // https://www.consultant.ru/document/cons_doc_LAW_61801/

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* Cambridge Analytica (CA) — британская частная компания, которая использовала продвинутые технологии анализа данных, собранных в социальных сетях, чтобы оказывать влияние на результаты выборов и референдумов.

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Chen D., Fraiberger S. P., Moakler R., Provost F. (2017). Enhancing Transparency and Control When Drawing Data-Driven Inferences About Individuals / Big DataVol. 5, No. 3 // https://doi.org/10.1089/big.2017.0074

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Duhigg C. (2012). How Companies Learn Your Secrets / The New York Times Magazine, February 16, 2012 // https://www.nytimes.com/2012/02/19/magazine/shopping-habits.html

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Basel Committee on Banking Supervision (2011). Basel III: A global regulatory framework for more resilient banks and banking systems // https://www.bis.org/publ/bcbs189.pdf

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Kang C. (2023). How Sam Altman Stormed Washington to Set the A.I. Agenda / The New York Times, June 7, 2023 // https://www.nytimes.com/2023/06/07/technology/sam-altman-ai-regulations.html

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Kang C. (2023). OpenAI’s Sam Altman Urges A.I. Regulation in Senate Hearing / The New York Times, May 16, 2023 // https://www.nytimes.com/2023/05/16/technology/openai-altman-artificial-intelligence-regulation.html

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* Пер. Н. Сосновской.

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Schmidhuber J. (2003). Goedel Machines: Self-Referential Universal Problem Solvers Making Provably Optimal Self-Improvements // https://arxiv.org/abs/cs/0309048

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Feurer M., Eggensperger K., Falkner S., Lindauer M., Hutter F. (2020). Auto-Sklearn 2.0: The Next Generation // https://arxiv.org/abs/2007.04074

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Kotthoff L., Thornton C., Hoos H. H., Hutter F., Leyton-Brown K. (2016). Auto-WEKA 2.0: Automatic model selection and hyperparameter optimization in WEKA / Journal of Machine Learning Research, Vol. 17 (2016) // http://www.cs.ubc.ca/labs/beta/Projects/autoweka/papers/16-599.pdf

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Erickson N., Mueller J., Shirkov A., Zhang H., Larroy P., Li M., Smola A. (2020). AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data // https://arxiv.org/abs/2003.06505

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Arora A., Candel A., Lanford J., LeDell E., Parmar V. (Oct. 2016). Deep Learning with H2O / http://docs.h2o.ai/h2o/latest-stable/h2o-docs/booklets/DeepLearningBooklet.pdf

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Click C., Lanford J., Malohlava M., Parmar V., Roark H. (Oct. 2016). Gradient Boosted Models with H2O / http://docs.h2o.ai/h2o/latest-stable/h2o-docs/booklets/GBMBooklet.pdf

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Le T. T., Fu W., Moore J. H. (2020). Scaling tree-based automated machine learning to biomedical big data with a feature set selector / Bioinformatics, Vol. 36 (1), pp. 250—256 // https://doi.org/10.1093/bioinformatics/btz470

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Wang C., Wu Q. (2019). FLO: Fast and Lightweight Hyperparameter Optimization for AutoML // https://arxiv.org/abs/1911.04706

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Prokhorenkova L., Gusev G., Vorobev A., Dorogush A. V., Gulin A. (2017). CatBoost: unbiased boosting with categorical features // https://arxiv.org/abs/1706.09516

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Zoph B., Le Q. V. (2016). Neural Architecture Search with Reinforcement Learning // https://arxiv.org/abs/1611.01578

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Real E., Moore S., Selle A.,