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Статті в журналах з теми "Interpretable AI"
Sathyan, Anoop, Abraham Itzhak Weinberg, and Kelly Cohen. "Interpretable AI for bio-medical applications." Complex Engineering Systems 2, no. 4 (2022): 18. http://dx.doi.org/10.20517/ces.2022.41.
Повний текст джерелаJia, Xun, Lei Ren, and Jing Cai. "Clinical implementation of AI technologies will require interpretable AI models." Medical Physics 47, no. 1 (November 19, 2019): 1–4. http://dx.doi.org/10.1002/mp.13891.
Повний текст джерелаXu, Wei, Jianshan Sun, and Mengxiang Li. "Guest editorial: Interpretable AI-enabled online behavior analytics." Internet Research 32, no. 2 (March 15, 2022): 401–5. http://dx.doi.org/10.1108/intr-04-2022-683.
Повний текст джерелаSkirzyński, Julian, Frederic Becker, and Falk Lieder. "Automatic discovery of interpretable planning strategies." Machine Learning 110, no. 9 (April 9, 2021): 2641–83. http://dx.doi.org/10.1007/s10994-021-05963-2.
Повний текст джерелаTomsett, Richard, Alun Preece, Dave Braines, Federico Cerutti, Supriyo Chakraborty, Mani Srivastava, Gavin Pearson, and Lance Kaplan. "Rapid Trust Calibration through Interpretable and Uncertainty-Aware AI." Patterns 1, no. 4 (July 2020): 100049. http://dx.doi.org/10.1016/j.patter.2020.100049.
Повний текст джерелаHerzog, Christian. "On the risk of confusing interpretability with explicability." AI and Ethics 2, no. 1 (December 9, 2021): 219–25. http://dx.doi.org/10.1007/s43681-021-00121-9.
Повний текст джерелаSchmidt Nordmo, Tor-Arne, Ove Kvalsvik, Svein Ove Kvalsund, Birte Hansen, and Michael A. Riegler. "Fish AI." Nordic Machine Intelligence 2, no. 2 (June 2, 2022): 1–3. http://dx.doi.org/10.5617/nmi.9657.
Повний текст джерелаPark, Sungjoon, Akshat Singhal, Erica Silva, Jason F. Kreisberg, and Trey Ideker. "Abstract 1159: Predicting clinical drug responses using a few-shot learning-based interpretable AI." Cancer Research 82, no. 12_Supplement (June 15, 2022): 1159. http://dx.doi.org/10.1158/1538-7445.am2022-1159.
Повний текст джерелаBaşağaoğlu, Hakan, Debaditya Chakraborty, Cesar Do Lago, Lilianna Gutierrez, Mehmet Arif Şahinli, Marcio Giacomoni, Chad Furl, Ali Mirchi, Daniel Moriasi, and Sema Sevinç Şengör. "A Review on Interpretable and Explainable Artificial Intelligence in Hydroclimatic Applications." Water 14, no. 8 (April 11, 2022): 1230. http://dx.doi.org/10.3390/w14081230.
Повний текст джерелаDemajo, Lara Marie, Vince Vella, and Alexiei Dingli. "An Explanation Framework for Interpretable Credit Scoring." International Journal of Artificial Intelligence & Applications 12, no. 1 (January 31, 2021): 19–38. http://dx.doi.org/10.5121/ijaia.2021.12102.
Повний текст джерелаДисертації з теми "Interpretable AI"
Gustafsson, Sebastian. "Interpretable serious event forecasting using machine learning and SHAP." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-444363.
Повний текст джерелаExakta prognoser är viktiga inom flera områden av ekonomisk, vetenskaplig, kommersiell och industriell verksamhet. Det finns få tidigare studier där man använt prognosmetoder för att förutsäga allvarliga händelser. Denna avhandling syftar till att undersöka två saker, för det första om maskininlärningsmodeller kan användas för att förutse allvarliga händelser. För det andra, om modellerna kunde göras tolkbara. Med tanke på dessa mål var metoden att formulera två prognosuppgifter för modellerna och sedan använda Python-ramverket SHAP för att göra dem tolkbara. Den första uppgiften var att förutsäga om en allvarlig händelse kommer att ske under de kommande åtta timmarna. Den andra uppgiften var att förutse hur många allvarliga händelser som kommer att hända under de kommande sex timmarna. GBDT- och LSTM-modeller implementerades, utvärderades och jämfördes för båda uppgifterna. Med tanke på problemkomplexiteten i att förutspå framtiden matchar resultaten de från tidigare relaterad forskning. På klassificeringsuppgiften uppnådde den bäst presterande modellen en träffsäkerhet på 71,6%, och på regressionsuppgiften missade den i genomsnitt med mindre än 1 i antal förutspådda allvarliga händelser.
Joel, Viklund. "Explaining the output of a black box model and a white box model: an illustrative comparison." Thesis, Uppsala universitet, Filosofiska institutionen, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-420889.
Повний текст джерелаNorrie, Christian. "Explainable AI techniques for sepsis diagnosis : Evaluating LIME and SHAP through a user study." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-19845.
Повний текст джерелаFjellström, Lisa. "The Contribution of Visual Explanations in Forensic Investigations of Deepfake Video : An Evaluation." Thesis, Umeå universitet, Institutionen för datavetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-184671.
Повний текст джерелаGridelli, Eleonora. "Interpretabilità nel Machine Learning tramite modelli di ottimizzazione discreta." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23216/.
Повний текст джерелаBalayan, Vladimir. "Human-Interpretable Explanations for Black-Box Machine Learning Models: An Application to Fraud Detection." Master's thesis, 2020. http://hdl.handle.net/10362/130774.
Повний текст джерелаA Aprendizagem de Máquina (AM) tem sido cada vez mais utilizada para ajudar os humanos a tomar decisões de alto risco numa vasta gama de áreas, desde política até à justiça criminal, educação, saúde e serviços financeiros. Porém, é muito difícil para os humanos perceber a razão da decisão do modelo de AM, prejudicando assim a confiança no sistema. O campo da Inteligência Artificial Explicável (IAE) surgiu para enfrentar este problema, visando desenvolver métodos para tornar as “caixas-pretas” mais interpretáveis, embora ainda sem grande avanço. Além disso, os métodos de explicação mais populares — LIME and SHAP — produzem explicações de muito baixo nível, sendo de utilidade limitada para pessoas sem conhecimento de AM. Este trabalho foi desenvolvido na Feedzai, a fintech que usa a AM para prevenir crimes financeiros. Um dos produtos da Feedzai é uma aplicação de gestão de casos, usada por analistas de fraude. Estes são especialistas no domínio treinados para procurar evidências suspeitas em transações financeiras, contudo não tendo o conhecimento em AM, os métodos de IAE atuais não satisfazem as suas necessidades de informação. Para resolver isso, apresentamos JOEL, a framework baseada em rede neuronal para aprender conjuntamente a tarefa de tomada de decisão e as explicações associadas. A JOEL é orientada a especialistas de domínio que não têm conhecimento técnico profundo de AM, fornecendo informações de alto nível sobre as previsões do modelo, que muito se assemelham ao raciocínio dos próprios especialistas. Ademais, ao recolher o feedback de especialistas certificados (ensino humano), promovemos explicações contínuas e de melhor qualidade. Por último, recorremos a mapeamentos semânticos entre sistemas legados e taxonomias de domínio para anotar automaticamente um conjunto de dados, superando a ausência de anotações humanas baseadas em conceitos. Validamos a JOEL empiricamente em um conjunto de dados de detecção de fraude do mundo real, na Feedzai. Mostramos que a JOEL pode generalizar as explicações aprendidas no conjunto de dados inicial e que o ensino humano é capaz de melhorar a qualidade da previsão das explicações.
Книги з теми "Interpretable AI"
Guerrini, Mauro. De bibliothecariis. Edited by Tiziana Stagi. Florence: Firenze University Press, 2017. http://dx.doi.org/10.36253/978-88-6453-559-3.
Повний текст джерелаThampi, Ajay. Interpretable AI: Building Explainable Machine Learning Systems. Manning Publications Co. LLC, 2022.
Знайти повний текст джерелаCappelen, Herman, and Josh Dever. Making AI Intelligible. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780192894724.001.0001.
Повний текст джерелаExplainable Fuzzy Systems: Paving the Way from Interpretable Fuzzy Systems to Explainable AI Systems. Springer International Publishing AG, 2021.
Знайти повний текст джерелаExplainable Fuzzy Systems: Paving the Way from Interpretable Fuzzy Systems to Explainable AI Systems. Springer International Publishing AG, 2022.
Знайти повний текст джерелаBellodi Ansaloni, Anna. L’arte dell’avvocato, actor veritatis. Bononia University Press, 2021. http://dx.doi.org/10.30682/sg279.
Повний текст джерелаЧастини книг з теми "Interpretable AI"
Elton, Daniel C. "Self-explaining AI as an Alternative to Interpretable AI." In Artificial General Intelligence, 95–106. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-52152-3_10.
Повний текст джерелаBastani, Osbert, Jeevana Priya Inala, and Armando Solar-Lezama. "Interpretable, Verifiable, and Robust Reinforcement Learning via Program Synthesis." In xxAI - Beyond Explainable AI, 207–28. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04083-2_11.
Повний текст джерелаPreuer, Kristina, Günter Klambauer, Friedrich Rippmann, Sepp Hochreiter, and Thomas Unterthiner. "Interpretable Deep Learning in Drug Discovery." In Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, 331–45. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-28954-6_18.
Повний текст джерелаBewley, Tom, Jonathan Lawry, and Arthur Richards. "Modelling Agent Policies with Interpretable Imitation Learning." In Trustworthy AI - Integrating Learning, Optimization and Reasoning, 180–86. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-73959-1_16.
Повний текст джерелаSchütt, Kristof T., Michael Gastegger, Alexandre Tkatchenko, and Klaus-Robert Müller. "Quantum-Chemical Insights from Interpretable Atomistic Neural Networks." In Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, 311–30. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-28954-6_17.
Повний текст джерелаMacDonald, Samual, Kaiah Steven, and Maciej Trzaskowski. "Interpretable AI in Healthcare: Enhancing Fairness, Safety, and Trust." In Artificial Intelligence in Medicine, 241–58. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1223-8_11.
Повний текст джерелаMallia, Natalia, Alexiei Dingli, and Foaad Haddod. "MIRAI: A Modifiable, Interpretable, and Rational AI Decision System." In Studies in Computational Intelligence, 127–41. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-61045-6_10.
Повний текст джерелаDinu, Marius-Constantin, Markus Hofmarcher, Vihang P. Patil, Matthias Dorfer, Patrick M. Blies, Johannes Brandstetter, Jose A. Arjona-Medina, and Sepp Hochreiter. "XAI and Strategy Extraction via Reward Redistribution." In xxAI - Beyond Explainable AI, 177–205. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04083-2_10.
Повний текст джерелаHong, Seunghoon, Dingdong Yang, Jongwook Choi, and Honglak Lee. "Interpretable Text-to-Image Synthesis with Hierarchical Semantic Layout Generation." In Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, 77–95. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-28954-6_5.
Повний текст джерелаAdadi, Amina, and Mohammed Berrada. "Explainable AI for Healthcare: From Black Box to Interpretable Models." In Embedded Systems and Artificial Intelligence, 327–37. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-0947-6_31.
Повний текст джерелаТези доповідей конференцій з теми "Interpretable AI"
Sengoz, Nilgun, and Tuncay Yigit. "Towards Third Generation AI: Explainable and Interpretable AI." In 2022 7th International Conference on Computer Science and Engineering (UBMK). IEEE, 2022. http://dx.doi.org/10.1109/ubmk55850.2022.9919510.
Повний текст джерелаDemajo, Lara Marie, Vince Vella, and Alexiei Dingli. "Explainable AI for Interpretable Credit Scoring." In 10th International Conference on Advances in Computing and Information Technology (ACITY 2020). AIRCC Publishing Corporation, 2020. http://dx.doi.org/10.5121/csit.2020.101516.
Повний текст джерелаCustode, Leonardo Lucio, and Giovanni Iacca. "Interpretable AI for policy-making in pandemics." In GECCO '22: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3520304.3533959.
Повний текст джерелаGuidotti, Riccardo, and Anna Monreale. "Designing Shapelets for Interpretable Data-Agnostic Classification." In AIES '21: AAAI/ACM Conference on AI, Ethics, and Society. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3461702.3462553.
Повний текст джерелаZhang, Wei, Brian Barr, and John Paisley. "An Interpretable Deep Classifier for Counterfactual Generation." In ICAIF '22: 3rd ACM International Conference on AI in Finance. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3533271.3561722.
Повний текст джерелаIgnatiev, Alexey, Joao Marques-Silva, Nina Narodytska, and Peter J. Stuckey. "Reasoning-Based Learning of Interpretable ML Models." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/608.
Повний текст джерелаVerma, Pulkit, Shashank Rao Marpally, and Siddharth Srivastava. "Discovering User-Interpretable Capabilities of Black-Box Planning Agents." In 19th International Conference on Principles of Knowledge Representation and Reasoning {KR-2022}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/kr.2022/36.
Повний текст джерелаKim, Tae Wan, and Bryan R. Routledge. "Informational Privacy, A Right to Explanation, and Interpretable AI." In 2018 IEEE Symposium on Privacy-Aware Computing (PAC). IEEE, 2018. http://dx.doi.org/10.1109/pac.2018.00013.
Повний текст джерелаPreece, Alun, Dan Harborne, Ramya Raghavendra, Richard Tomsett, and Dave Braines. "Provisioning Robust and Interpretable AI/ML-Based Service Bundles." In MILCOM 2018 - IEEE Military Communications Conference. IEEE, 2018. http://dx.doi.org/10.1109/milcom.2018.8599838.
Повний текст джерелаPitroda, Vidhi, Mostafa M. Fouda, and Zubair Md Fadlullah. "An Explainable AI Model for Interpretable Lung Disease Classification." In 2021 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS). IEEE, 2021. http://dx.doi.org/10.1109/iotais53735.2021.9628573.
Повний текст джерелаЗвіти організацій з теми "Interpretable AI"
Chen, Thomas, Biprateep Dey, Aishik Ghosh, Michael Kagan, Brian Nord, and Nesar Ramachandra. Interpretable Uncertainty Quantification in AI for HEP. Office of Scientific and Technical Information (OSTI), August 2022. http://dx.doi.org/10.2172/1886020.
Повний текст джерелаZhu, Qing, William Riley, and James Randerson. Improve wildfire predictability driven by extreme water cycle with interpretable physically-guided ML/AI. Office of Scientific and Technical Information (OSTI), April 2021. http://dx.doi.org/10.2172/1769720.
Повний текст джерела