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Can we open the black box of AI?

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Cited by:

  1. Maidana, Renan G. & Parhizkar, Tarannom & Martin, Gabriel San & Utne, Ingrid B., 2024. "Dynamic probabilistic risk assessment with K-shortest-paths planning for generating discrete dynamic event trees," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
  2. Chenfeng Yan & Quan Chen & Xinyue Zhou & Xin Dai & Zhilin Yang, 2024. "When the Automated fire Backfires: The Adoption of Algorithm-based HR Decision-making Could Induce Consumer’s Unfavorable Ethicality Inferences of the Company," Journal of Business Ethics, Springer, vol. 190(4), pages 841-859, April.
  3. Martin Eling & Davide Nuessle & Julian Staubli, 2022. "The impact of artificial intelligence along the insurance value chain and on the insurability of risks," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 47(2), pages 205-241, April.
  4. Haque, AKM Bahalul & Islam, A.K.M. Najmul & Mikalef, Patrick, 2023. "Explainable Artificial Intelligence (XAI) from a user perspective: A synthesis of prior literature and problematizing avenues for future research," Technological Forecasting and Social Change, Elsevier, vol. 186(PA).
  5. Canellas, Marc & Haga, Rachel, 2017. "Framing Human-Automation Regulation: A New Modus Operandi from Cognitive Engineering," LawArXiv yu2h3, Center for Open Science.
  6. Brian G Booth & Eva Hoefnagels & Toon Huysmans & Jan Sijbers & Noël L W Keijsers, 2020. "PAPPI: Personalized analysis of plantar pressure images using statistical modelling and parametric mapping," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-22, February.
  7. Jacob Dexe & Ulrik Franke & Alexander Rad, 2021. "Transparency and insurance professionals: a study of Swedish insurance practice attitudes and future development," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 46(4), pages 547-572, October.
  8. Hui Niu & Siyuan Li & Jian Li, 2022. "MetaTrader: An Reinforcement Learning Approach Integrating Diverse Policies for Portfolio Optimization," Papers 2210.01774, arXiv.org.
  9. Kashyap, Ravi, 2021. "Artificial Intelligence: A Child’s Play," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
  10. Alireza Rezazadeh & Yasamin Jafarian & Ali Kord, 2022. "Explainable Ensemble Machine Learning for Breast Cancer Diagnosis Based on Ultrasound Image Texture Features," Forecasting, MDPI, vol. 4(1), pages 1-13, February.
  11. Gao, Yuan & Miyata, Shohei & Akashi, Yasunori, 2023. "How to improve the application potential of deep learning model in HVAC fault diagnosis: Based on pruning and interpretable deep learning method," Applied Energy, Elsevier, vol. 348(C).
  12. Alwosheel, Ahmad & van Cranenburgh, Sander & Chorus, Caspar G., 2018. "Is your dataset big enough? Sample size requirements when using artificial neural networks for discrete choice analysis," Journal of choice modelling, Elsevier, vol. 28(C), pages 167-182.
  13. Eduardo Graells-Garrido & Vanessa Peña-Araya & Loreto Bravo, 2020. "Adoption-Driven Data Science for Transportation Planning: Methodology, Case Study, and Lessons Learned," Sustainability, MDPI, vol. 12(15), pages 1-17, July.
  14. Dominic Chalmers & Niall G. MacKenzie & Sara Carter, 2021. "Artificial Intelligence and Entrepreneurship: Implications for Venture Creation in the Fourth Industrial Revolution," Entrepreneurship Theory and Practice, , vol. 45(5), pages 1028-1053, September.
  15. O’Dwyer, Edward & Pan, Indranil & Acha, Salvador & Shah, Nilay, 2019. "Smart energy systems for sustainable smart cities: Current developments, trends and future directions," Applied Energy, Elsevier, vol. 237(C), pages 581-597.
  16. Nir Douer & Joachim Meyer, 2023. "Quantifying Retrospective Human Responsibility in Intelligent Systems," Papers 2308.01752, arXiv.org.
  17. Hsu, Chia-Wei & Xiong, Rui & Chen, Nan-Yow & Li, Ju & Tsou, Nien-Ti, 2022. "Deep neural network battery life and voltage prediction by using data of one cycle only," Applied Energy, Elsevier, vol. 306(PB).
  18. Schuwirth, Nele & Borgwardt, Florian & Domisch, Sami & Friedrichs, Martin & Kattwinkel, Mira & Kneis, David & Kuemmerlen, Mathias & Langhans, Simone D. & Martínez-López, Javier & Vermeiren, Peter, 2019. "How to make ecological models useful for environmental management," Ecological Modelling, Elsevier, vol. 411(C).
  19. Pei-Chen Tsai & Tsung-Hua Lee & Kun-Chi Kuo & Fang-Yi Su & Tsung-Lu Michael Lee & Eliana Marostica & Tomotaka Ugai & Melissa Zhao & Mai Chan Lau & Juha P. Väyrynen & Marios Giannakis & Yasutoshi Takas, 2023. "Histopathology images predict multi-omics aberrations and prognoses in colorectal cancer patients," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
  20. Vasile MAZILESCU & Adrian MICU, 2019. "Technologies that through Synergic Development can support the Intelligent Automation of Business Processes," Economics and Applied Informatics, "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration, issue 2, pages 91-100.
  21. Emma Dahlin, 2021. "Mind the gap! On the future of AI research," Palgrave Communications, Palgrave Macmillan, vol. 8(1), pages 1-4, December.
  22. Laith T. Khrais, 2020. "Role of Artificial Intelligence in Shaping Consumer Demand in E-Commerce," Future Internet, MDPI, vol. 12(12), pages 1-14, December.
  23. Pujin Wang & Jianzhuang Xiao & Ken’ichi Kawaguchi & Lichen Wang, 2022. "Automatic Ceiling Damage Detection in Large-Span Structures Based on Computer Vision and Deep Learning," Sustainability, MDPI, vol. 14(6), pages 1-24, March.
  24. Michael Meiser & Ingo Zinnikus, 2024. "A Survey on the Use of Synthetic Data for Enhancing Key Aspects of Trustworthy AI in the Energy Domain: Challenges and Opportunities," Energies, MDPI, vol. 17(9), pages 1-29, April.
  25. Paula Laccourreye & Concha Bielza & Pedro Larrañaga, 2022. "Explainable Machine Learning for Longitudinal Multi-Omic Microbiome," Mathematics, MDPI, vol. 10(12), pages 1-23, June.
  26. Augusto Anguita-Ruiz & Alberto Segura-Delgado & Rafael Alcalá & Concepción M Aguilera & Jesús Alcalá-Fdez, 2020. "eXplainable Artificial Intelligence (XAI) for the identification of biologically relevant gene expression patterns in longitudinal human studies, insights from obesity research," PLOS Computational Biology, Public Library of Science, vol. 16(4), pages 1-34, April.
  27. Buxmann, Peter & Ellenrieder, Sara, 2024. "Unlocking AI’s Potential : Human Collaboration as the Catalyst," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 149346, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
  28. Sander Cranenburgh & Marco Kouwenhoven, 2021. "An artificial neural network based method to uncover the value-of-travel-time distribution," Transportation, Springer, vol. 48(5), pages 2545-2583, October.
  29. Lee, Donghyun & Kim, Mingyu & Lee, Beomhui & Chae, Sangwon & Kwon, Sungjun & Kang, Sungwon, 2022. "Integrated explainable deep learning prediction of harmful algal blooms," Technological Forecasting and Social Change, Elsevier, vol. 185(C).
  30. Roman Lukyanenko & Wolfgang Maass & Veda C. Storey, 2022. "Trust in artificial intelligence: From a Foundational Trust Framework to emerging research opportunities," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(4), pages 1993-2020, December.
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