Relevant bibliographies by topics / Explainable artifical intelligence

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Explainable artifical intelligence'

Author: Grafiati
Published: 11 March 2023
Last updated: 31 July 2025

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Journal articles on the topic "Explainable artifical intelligence"

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Amitha, T., P. Shobana, M. Jayashree, and R. Rajalakshmi. "Explainable Artificial Intelligence for Safely Health Care." International Journal of Science and Research (IJSR) 14, no. 1 (2025): 1155–60. https://doi.org/10.21275/sr25124103755.

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Ridley, Michael. "Explainable Artificial Intelligence." Ethics of Artificial Intelligence, no. 299 (September 19, 2019): 28–46. http://dx.doi.org/10.29242/rli.299.3.

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Rousseau, Axel-Jan, Melvin Geubbelmans, Dirk Valkenborg, and Tomasz Burzykowski. "Explainable artificial intelligence." American Journal of Orthodontics and Dentofacial Orthopedics 165, no. 4 (2024): 491–94. http://dx.doi.org/10.1016/j.ajodo.2024.01.006.

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Gunning, David, Mark Stefik, Jaesik Choi, Timothy Miller, Simone Stumpf, and Guang-Zhong Yang. "XAI—Explainable artificial intelligence." Science Robotics 4, no. 37 (2019): eaay7120. http://dx.doi.org/10.1126/scirobotics.aay7120.

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Sewada, Ranu, Ashwani Jangid, Piyush Kumar, and Neha Mishra. "Explainable Artificial Intelligence (XAI)." Journal of Nonlinear Analysis and Optimization 13, no. 01 (2023): 41–47. http://dx.doi.org/10.36893/jnao.2022.v13i02.041-047.

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Explainable Artificial Intelligence (XAI) has emerged as a critical facet in the realm of machine learning and artificial intelligence, responding to the increasing complexity of models, particularly deep neural networks, and the subsequent need for transparent decision making processes. This research paper delves into the essence of XAI, unraveling its significance across diverse domains such as healthcare, finance, and criminal justice. As a countermeasure to the opacity of intricate models, the paper explores various XAI methods and techniques, including LIME and SHAP, weighing their interp
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Suresh, S. E., and K. Venkateswara Reddy. "Explainable Artificial Intelligence Model for Predictive Maintenance in Smart Agricultural Facilities." International Journal of Research Publication and Reviews 6, no. 5 (2025): 11806–8. https://doi.org/10.55248/gengpi.6.0525.18125.

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Chauhan, Tavishee, and Sheetal Sonawane. "Contemplation of Explainable Artificial Intelligence Techniques." International Journal on Recent and Innovation Trends in Computing and Communication 10, no. 4 (2022): 65–71. http://dx.doi.org/10.17762/ijritcc.v10i4.5538.

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Machine intelligence and data science are two disciplines that are attempting to develop Artificial Intelligence. Explainable AI is one of the disciplines being investigated, with the goal of improving the transparency of black-box systems. This article aims to help people comprehend the necessity for Explainable AI, as well as the various methodologies used in various areas, all in one place. This study clarified how model interpretability and Explainable AI work together. This paper aims to investigate the Explainable artificial intelligence approaches their applications in multiple domains.
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Moosavi, Sajad, Maryam Farajzadeh-Zanjani, Roozbeh Razavi-Far, Vasile Palade, and Mehrdad Saif. "Explainable AI in Manufacturing and Industrial Cyber–Physical Systems: A Survey." Electronics 13, no. 17 (2024): 3497. http://dx.doi.org/10.3390/electronics13173497.

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This survey explores applications of explainable artificial intelligence in manufacturing and industrial cyber–physical systems. As technological advancements continue to integrate artificial intelligence into critical infrastructure and industrial processes, the necessity for clear and understandable intelligent models becomes crucial. Explainable artificial intelligence techniques play a pivotal role in enhancing the trustworthiness and reliability of intelligent systems applied to industrial systems, ensuring human operators can comprehend and validate the decisions made by these intelligen
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Abdelmonem, Ahmed, and Nehal N. Mostafa. "Interpretable Machine Learning Fusion and Data Analytics Models for Anomaly Detection." Fusion: Practice and Applications 3, no. 1 (2021): 54–69. http://dx.doi.org/10.54216/fpa.030104.

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Explainable artificial intelligence received great research attention in the past few years during the widespread of Black-Box techniques in sensitive fields such as medical care, self-driving cars, etc. Artificial intelligence needs explainable methods to discover model biases. Explainable artificial intelligence will lead to obtaining fairness and Transparency in the model. Making artificial intelligence models explainable and interpretable is challenging when implementing black-box models. Because of the inherent limitations of collecting data in its raw form, data fusion has become a popul
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Darwish, Ashraf. "Explainable Artificial Intelligence: A New Era of Artificial Intelligence." Digital Technologies Research and Applications 1, no. 1 (2022): 1. http://dx.doi.org/10.54963/dtra.v1i1.29.

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Recently, Artificial Intelligence (AI) has emerged as an emerging with advanced methodologies and innovative applications. With the rapid advancement of AI concepts and technologies, there has been a recent trend to add interpretability and explainability to the paradigm. With the increasing complexity of AI applications, their a relationship with data analytics, and the ubiquity of demanding applications in a variety of critical applications such as medicine, defense, justice and autonomous vehicles , there is an increasing need to associate the results with sound explanations to domain exper
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Dissertations / Theses on the topic "Explainable artifical intelligence"

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Nilsson, Linus. "Explainable Artificial Intelligence for Reinforcement Learning Agents." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-294162.

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Following the success that machine learning has enjoyed over the last decade, reinforcement learning has become a prime research area for automation and solving complex tasks. Ranging from playing video games at a professional level to robots collaborating in picking goods in warehouses, the applications of reinforcement learning are numerous. The systems are however, very complex and the understanding of why the reinforcement learning agents solve the tasks given to them in certain ways are still largely unknown to the human observer. This makes the actual use of the agents limited to non-cri
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Elguendouze, Sofiane. "Explainable Artificial Intelligence approaches for Image Captioning." Electronic Thesis or Diss., Orléans, 2024. http://www.theses.fr/2024ORLE1003.

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L'évolution rapide des modèles de sous-titrage d'images, impulsée par l'intégration de techniques d'apprentissage profond combinant les modalités image et texte, a conduit à des systèmes de plus en plus complexes. Cependant, ces modèles fonctionnent souvent comme des boîtes noires, incapables de fournir des explications transparentes de leurs décisions. Cette thèse aborde l'explicabilité des systèmes de sous-titrage d'images basés sur des architectures Encodeur-Attention-Décodeur, et ce à travers quatre aspects. Premièrement, elle explore le concept d'espace latent, s'éloignant ainsi des appro
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El, Qadi El Haouari Ayoub. "An EXplainable Artificial Intelligence Credit Rating System." Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS486.

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Au cours des dernières années, le déficit de financement du commerce a atteint le chiffre alarmant de 1 500 milliards de dollars, soulignant une crise croissante dans le commerce mondial. Ce déficit est particulièrement préjudiciable aux petites et moyennes entreprises (PME), qui éprouvent souvent des difficultés à accéder au financement du commerce. Les systèmes traditionnels d'évaluation du crédit, qui constituent l'épine dorsale du finance-ment du commerce, ne sont pas toujours adaptés pour évaluer correctement la solvabilité des PME. Le terme "credit scoring" désigne les méthodes et techniques uti
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Vincenzi, Leonardo. "eXplainable Artificial Intelligence User Experience: contesto e stato dell’arte." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23338/.

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Il grande sviluppo del mondo dell’Intelligenza Artificiale unito alla sua vastissima applicazione in molteplici ambiti degli ultimi anni, ha portato a una sempre maggior richiesta di spiegabilità dei sistemi di Machine Learning. A seguito di questa necessità il campo dell’eXplainable Artificial Intelligence ha compiuto passi importanti verso la creazione di sistemi e metodi per rendere i sistemi intelligenti sempre più trasparenti e in un futuro prossimo, per garantire sempre più equità e sicurezza nelle decisioni prese dall’AI, si prevede una sempre più rigida regolamentazione verso la sua sp
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PICCHIOTTI, NICOLA. "Explainable Artificial Intelligence: an application to complex genetic diseases." Doctoral thesis, Università degli studi di Pavia, 2021. http://hdl.handle.net/11571/1447637.

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ABUKMEIL, MOHANAD. "UNSUPERVISED GENERATIVE MODELS FOR DATA ANALYSIS AND EXPLAINABLE ARTIFICIAL INTELLIGENCE." Doctoral thesis, Università degli Studi di Milano, 2022. http://hdl.handle.net/2434/889159.

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For more than a century, the methods of learning representation and the exploration of the intrinsic structures of data have developed remarkably and currently include supervised, semi-supervised, and unsupervised methods. However, recent years have witnessed the flourishing of big data, where typical dataset dimensions are high, and the data can come in messy, missing, incomplete, unlabeled, or corrupted forms. Consequently, discovering and learning the hidden structure buried inside such data becomes highly challenging. From this perspective, latent data analysis and dimensionality reductio
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Rouget, Thierry. "Learning explainable concepts in the presence of a qualitative model." Thesis, University of Ottawa (Canada), 1995. http://hdl.handle.net/10393/9762.

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This thesis addresses the problem of learning concept descriptions that are interpretable, or explainable. Explainability is understood as the ability to justify the learned concept in terms of the existing background knowledge. The starting point for the work was an existing system that would induce only fully explainable rules. The system performed well when the model used during induction was complete and correct. In practice, however, models are likely to be imperfect, i.e. incomplete and incorrect. We report here a new approach that achieves explainability with imperfect models. The basis
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Gjeka, Mario. "Uno strumento per le spiegazioni di sistemi di Explainable Artificial Intelligence." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2020.

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L'obiettivo di questa tesi è quello di mostrare l’importanza delle spiegazioni in un sistema intelligente. Il bisogno di avere un'intelligenza artificiale spiegabile e trasparente sta crescendo notevolmente, esigenza evidenziata dalla ricerca delle aziende di sviluppare sistemi informatici intelligenti trasparenti e spiegabili.
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Ketata, Firas. "Risk prediction of endocrine diseases using data science and explainable artificial intelligence." Electronic Thesis or Diss., Bourgogne Franche-Comté, 2024. http://www.theses.fr/2024UBFCD022.

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L'objectif de cette thèse est de prédire le risque de maladies endocriniennes à l'aide de la science des données et de l'apprentissage automatique. L'idée est d'exploiter cette identification de risque pour aider les médecins à gérer les ressources financières et personnaliser le traitement des anomalies glucidiques chez les patients atteints de bêta-thalassémie majeure, ainsi que pour le dépistage du syndrome métabolique chez les adolescents. Une étude d'explicabilité des prédictions a été développée dans cette thèse pour évaluer la fiabilité de la prédiction des anomalies glucidiques et pour
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Amarasinghe, Kasun. "Explainable Neural Networks based Anomaly Detection for Cyber-Physical Systems." VCU Scholars Compass, 2019. https://scholarscompass.vcu.edu/etd/6091.

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Cyber-Physical Systems (CPSs) are the core of modern critical infrastructure (e.g. power-grids) and securing them is of paramount importance. Anomaly detection in data is crucial for CPS security. While Artificial Neural Networks (ANNs) are strong candidates for the task, they are seldom deployed in safety-critical domains due to the perception that ANNs are black-boxes. Therefore, to leverage ANNs in CPSs, cracking open the black box through explanation is essential. The main objective of this dissertation is developing explainable ANN-based Anomaly Detection Systems for Cyber-Physical System
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Books on the topic "Explainable artifical intelligence"

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Longo, Luca, ed. Explainable Artificial Intelligence. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44064-9.

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Longo, Luca, ed. Explainable Artificial Intelligence. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44070-0.

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Longo, Luca, ed. Explainable Artificial Intelligence. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44067-0.

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Longo, Luca, Sebastian Lapuschkin, and Christin Seifert, eds. Explainable Artificial Intelligence. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-63803-9.

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Longo, Luca, Sebastian Lapuschkin, and Christin Seifert, eds. Explainable Artificial Intelligence. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-63797-1.

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Longo, Luca, Sebastian Lapuschkin, and Christin Seifert, eds. Explainable Artificial Intelligence. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-63787-2.

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Longo, Luca, Sebastian Lapuschkin, and Christin Seifert, eds. Explainable Artificial Intelligence. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-63800-8.

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Gaur, Loveleen, and Biswa Mohan Sahoo. Explainable Artificial Intelligence for Intelligent Transportation Systems. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09644-0.

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Adadi, Amina, and Afaf Bouhoute. Explainable Artificial Intelligence for Intelligent Transportation Systems. CRC Press, 2023. http://dx.doi.org/10.1201/9781003324140.

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Krötzsch, Markus, and Daria Stepanova, eds. Reasoning Web. Explainable Artificial Intelligence. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31423-1.

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Book chapters on the topic "Explainable artifical intelligence"

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Mahto, Manoj Kumar. "Explainable artificial intelligence." In Explainable Artificial Intelligence for Autonomous Vehicles. CRC Press, 2024. http://dx.doi.org/10.1201/9781003502432-2.

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Pan, Zhixin, and Prabhat Mishra. "Explainable Artificial Intelligence." In Explainable AI for Cybersecurity. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-46479-9_2.

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Barezzani, Sergio. "Explainable Artificial Intelligence." In Encyclopedia of Cryptography, Security and Privacy. Springer Berlin Heidelberg, 2024. http://dx.doi.org/10.1007/978-3-642-27739-9_1826-1.

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de Santana, Maíra Araújo, Giselle Machado Magalhães Moreno, and Wellington Pinheiro dos Santos. "Explainable Artificial Intelligence." In Artificial Intelligence and Bioethics. CRC Press, 2025. https://doi.org/10.1201/9781032694771-8.

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Barezzani, Sergio. "Explainable Artificial Intelligence." In Encyclopedia of Cryptography, Security and Privacy. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-030-71522-9_1826.

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Arriagada-Bruneau, Gabriela, Claudia López, and Marcelo Mendoza. "Explainable Artificial Intelligence." In Ethics in Artificial Intelligence and Information Technologies. CRC Press, 2025. https://doi.org/10.1201/9781003454625-10.

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Dix, Alan, and Janet Finlay. "Explainable AI." In Artificial Intelligence, 2nd ed. Chapman and Hall/CRC, 2025. https://doi.org/10.1201/9781003082880-25.

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Samek, Wojciech, and Klaus-Robert Müller. "Towards Explainable Artificial Intelligence." In Explainable AI: Interpreting, Explaining and Visualizing Deep Learning. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-28954-6_1.

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Mohaghegh, Shahab D. "Explainable Artificial Intelligence (XAI)." In Artificial Intelligence for Science and Engineering Applications. CRC Press, 2024. http://dx.doi.org/10.1201/9781003369356-8.

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Mahalle, Parikshit Narendra, and Yashwant Sudhakar Ingle. "Explainable Artificial Intelligence Overview." In Explainable Artificial Intelligence: A Practical Guide. River Publishers, 2024. http://dx.doi.org/10.1201/9788770047142-1.

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Conference papers on the topic "Explainable artifical intelligence"

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Alduweib, Eman, Muhammad Abu Arqoub, and Waseem Alromema. "Explainable Artificial Intelligence for Business Intelligence." In 2025 1st International Conference on Computational Intelligence Approaches and Applications (ICCIAA). IEEE, 2025. https://doi.org/10.1109/icciaa65327.2025.11013313.

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Al, Samed, and Şeref Sağiroğlu. "A Review of Explainable Artificial Intelligence." In 2024 9th International Conference on Computer Science and Engineering (UBMK). IEEE, 2024. https://doi.org/10.1109/ubmk63289.2024.10773588.

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Verma, Neha, Ajay Kumar Varshney, Ritesh Kumar Singhal, Manu Priya Gaur, Ankit Garg, and Sanghamitra Das. "Explainable Artificial Intelligence (XAI) in Insurance." In 2025 International Conference on Pervasive Computational Technologies (ICPCT). IEEE, 2025. https://doi.org/10.1109/icpct64145.2025.10939062.

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Tan, Wenkai, Jayaprakash B. Shivakumar, Rishikesh Srinivasaraghavan Govindarajan, et al. "Explainable Artificial Intelligence for Antenna Sensor Modeling." In 2024 IEEE International Mediterranean Conference on Communications and Networking (MeditCom). IEEE, 2024. http://dx.doi.org/10.1109/meditcom61057.2024.10621273.

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Canti, Edoardo, Enrico Collini, Luciano Alessandro Ipsaro Palesi, and Paolo Nesi. "Comparing Techniques for Temporal Explainable Artificial Intelligence." In 2024 IEEE 10th International Conference on Big Data Computing Service and Machine Learning Applications (BigDataService). IEEE, 2024. http://dx.doi.org/10.1109/bigdataservice62917.2024.00019.

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Kaushal, Shradha, Sonali, Harsh Gaur, Gazab Bhati, and Arun Kumar Rai. "Explainable Artificial Intelligence for brain Tumor Detection." In 2024 2nd International Conference on Advances in Computation, Communication and Information Technology (ICAICCIT). IEEE, 2024. https://doi.org/10.1109/icaiccit64383.2024.10912262.

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Dissanayaka, Didula, Thumeera R. Wanasinghe, and Raymond G. Gosine. "Explainable Artificial intelligence for Autonomous UAV Navigation." In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2024. https://doi.org/10.1109/iros58592.2024.10801529.

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B, Savitha, Achyutha Prasad N, Sushmitha B. R, Megha R, Laxmi Laxmi, and LeenaShruthi Hm. "Explainable Artificial Intelligence Applications in Cyber Security." In 2024 1st International Conference on Advances in Computing, Communication and Networking (ICAC2N). IEEE, 2024. https://doi.org/10.1109/icac2n63387.2024.10895029.

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Amarasooriya, Rasini, Mark A. Gregory, and Shuo Li. "Explainable Artificial Intelligence for Computation Offloading Optimization." In 2024 34th International Telecommunication Networks and Applications Conference (ITNAC). IEEE, 2024. https://doi.org/10.1109/itnac62915.2024.10815558.

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Lin, C. W., P. C. Tsao, Ross Lee, et al. "Boost CPU Turbo Yield Utilizing Explainable Artificial Intelligence." In 2024 IEEE International Test Conference (ITC). IEEE, 2024. http://dx.doi.org/10.1109/itc51657.2024.00031.

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Reports on the topic "Explainable artifical intelligence"

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Core, Mark G., H. C. Lane, Michael van Lent, Dave Gomboc, Steve Solomon, and Milton Rosenberg. Building Explainable Artificial Intelligence Systems. Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada459166.

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Phillips, P. Jonathon, Carina A. Hahn, Peter C. Fontana, et al. Four Principles of Explainable Artificial Intelligence. National Institute of Standards and Technology, 2021. http://dx.doi.org/10.6028/nist.ir.8312.

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Walker, Cody, Vivek Agarwal, Linyu Lin, et al. Explainable Artificial Intelligence Technology for Predictive Maintenance. Office of Scientific and Technical Information (OSTI), 2023. http://dx.doi.org/10.2172/1998555.

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Warin, Thierry, and Sarah Elimam. GDP 5.0: Real-Time, Micro-Founded and Sustainable Metrics for Beyond-GDP Economic Assessment. CIRANO, 2025. https://doi.org/10.54932/wfji8791.

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Gross Domestic Product (GDP) remains the dominant yardstick for economic performance, yet its aggregated, nation-bound and market-exclusive nature obscures crucial dimensions of prosperity, equity and environmental sustainability. Building on recent advances in data science and the expanding “Beyond-GDP” literature, this article argues for a generational shift in economic measurement designated “GDP 5.0.” This new approach of GDP integrates high-frequency, geolocated micro-data with artificial-intelligence methods to generate real-time dashboards of economic activity, social welfare and planet
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