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Relevant bibliographies by topics / Explainable AI

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Academic literature on the topic 'Explainable AI'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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Journal articles on the topic "Explainable AI"

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Storey, Veda C., Roman Lukyanenko, Wolfgang Maass, and Jeffrey Parsons. "Explainable AI." Communications of the ACM 65, no. 4 (2022): 27–29. http://dx.doi.org/10.1145/3490699.

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Holzinger, Andreas. "Explainable AI (ex-AI)." Informatik-Spektrum 41, no. 2 (2018): 138–43. http://dx.doi.org/10.1007/s00287-018-1102-5.

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Matsuo, Tatsuru, Masaru Todoriki, and Shin-ichiro Tago. "2. Explainable AI." Journal of The Institute of Image Information and Television Engineers 74, no. 1 (2020): 30–34. http://dx.doi.org/10.3169/itej.74.30.

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Hind, Michael. "Explaining explainable AI." XRDS: Crossroads, The ACM Magazine for Students 25, no. 3 (2019): 16–19. http://dx.doi.org/10.1145/3313096.

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Pingel, Johanna. "Making AI Explainable." New Electronics 55, no. 10 (2022): 30–31. http://dx.doi.org/10.12968/s0047-9624(23)60440-7.

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Qiming, Xu, Feng Zheng, Gong Chenwei, et al. "Applications of Explainable AI in Natural Language Processing." Global Academic Frontiers 2, no. 3 (2024): 51–64. https://doi.org/10.5281/zenodo.12684705.

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This paper investigates and discusses the applications of explainable AI in natural language processing. It first analyzes the importance and current state of AI in natural language processing, then focuses on the role and advantages of explainable AI technology in this field. It compares explainable AI with traditional AI from various angles and elucidates the unique value of explainable AI in natural language processing. On this basis, suggestions for further improvements and applications of explainable AI are proposed to advance the field of natural language processing. Finally, the potenti

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Hafermalz, Ella, and Marleen Huysman. "Please Explain: Key Questions for Explainable AI research from an Organizational perspective." Morals & Machines 1, no. 2 (2021): 10–23. http://dx.doi.org/10.5771/2747-5174-2021-2-10.

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There is growing interest in explanations as an ethical and technical solution to the problem of 'opaque' AI systems. In this essay we point out that technical and ethical approaches to Explainable AI (XAI) have different assumptions and aims. Further, the organizational perspective is missing from this discourse. In response we formulate key questions for explainable AI research from an organizational perspective: 1) Who is the 'user' in Explainable AI? 2) What is the 'purpose' of an explanation in Explainable AI? and 3) Where does an explanation 'reside' in Explainable AI? Our aim is to prom

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Shah, Jyoti Kunal. "Explainable AI In Software Engineering: Enhancing Developer-AI Collaboration." American Journal of Engineering and Technology 06, no. 07 (2024): 99–108. https://doi.org/10.37547/tajet/volume06issue07-11.

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Artificial Intelligence (AI) tools are increasingly integrated into software engineering tasks such as code generation, defect prediction, and project planning. However, widespread adoption is hindered by developers’ skepticism toward opaque AI models that lack transparency. This paper explores the integration of Explainable AI (XAI) into software engineering to foster a “developer-in-the-loop” paradigm that enhances trust, understanding, and collaboration between developers and AI agents. We review existing research on XAI techniques applied to feature planning, debugging, and refactoring, an

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Zhang, Jiachi, Wenchao Zhou, and Benjamin E. Ujcich. "Provenance-Enabled Explainable AI." Proceedings of the ACM on Management of Data 2, no. 6 (2024): 1–27. https://doi.org/10.1145/3698826.

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Machine learning (ML) algorithms have advanced significantly in recent years, progressively evolving into artificial intelligence (AI) agents capable of solving complex, human-like intellectual challenges. Despite the advancements, the interpretability of these sophisticated models lags behind, with many ML architectures remaining "black boxes" that are too intricate and expansive for human interpretation. Recognizing this issue, there has been a revived interest in the field of explainable AI (XAI) aimed at explaining these opaque ML models. However, XAI tools often suffer from being tightly

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SANO, Takanori. "Explainable AI in Art." International Symposium on Affective Science and Engineering ISASE2024 (2024): 1–3. http://dx.doi.org/10.5057/isase.2024-c000043.

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Dissertations / Theses on the topic "Explainable AI"

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PASTOR, ELIANA. "Pattern-based algorithms for Explainable AI." Doctoral thesis, Politecnico di Torino, 2021. http://hdl.handle.net/11583/2942116.

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PANIGUTTI, Cecilia. "eXplainable AI for trustworthy healthcare applications." Doctoral thesis, Scuola Normale Superiore, 2022. https://hdl.handle.net/11384/125202.

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Acknowledging that AI will inevitably become a central element of clinical practice, this thesis investigates the role of eXplainable AI (XAI) techniques in developing trustworthy AI applications in healthcare. The first part of this thesis focuses on the societal, ethical, and legal aspects of the use of AI in healthcare. It first compares the different approaches to AI ethics worldwide and then focuses on the practical implications of the European ethical and legal guidelines for AI applications in healthcare. The second part of the thesis explores how XAI techniques can help meet thr

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Strineholm, Philippe. "Exploring Human-Robot Interaction Through Explainable AI Poetry Generation." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-54606.

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As the field of Artificial Intelligence continues to evolve into a tool of societal impact, a need of breaking its initial boundaries as a computer science discipline arises to also include different humanistic fields. The work presented in this thesis revolves around the role that explainable artificial intelligence has in human-robot interaction through the study of poetry generators. To better understand the scope of the project, a poetry generators study presents the steps involved in the development process and the evaluation methods. In the algorithmic development of poetry generators, t

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Stiff, Harald. "Explainable AI as a Defence Mechanism for Adversarial Examples." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-260347.

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Deep learning is the gold standard for image classification tasks. With its introduction came many impressive improvements in computer vision outperforming all of the earlier machine learning models. However, in contrast to the success it has been shown that deep neural networks are easily fooled by adversarial examples, data that have been modified slightly to cause the neural networks to make incorrect classifications. This significant disadvantage has caused an increased doubt in neural networks and it has been questioned whether or not they are safe to use in practice. In this thesis we pr

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FRACCAROLI, MICHELE. "Explainable Deep Learning." Doctoral thesis, Università degli studi di Ferrara, 2023. https://hdl.handle.net/11392/2503729.

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Il grande successo che il Deep Learning ha ottenuto in ambiti strategici per la nostra società quali l'industria, la difesa, la medicina etc., ha portanto sempre più realtà a investire ed esplorare l'utilizzo di questa tecnologia. Ormai si possono trovare algoritmi di Machine Learning e Deep Learning quasi in ogni ambito della nostra vita. Dai telefoni, agli elettrodomestici intelligenti fino ai veicoli che guidiamo. Quindi si può dire che questa tecnologia pervarsiva è ormai a contatto con le nostre vite e quindi dobbiamo confrontarci con essa. Da questo nasce l’eXplainable Artificial Intelli

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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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Benedetti, Riccardo. "Neuroimaging e disturbo dello spettro autistico: classificazione con approccio explainable AI." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.

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Il disturbo dello spettro autistico (Autistic Spectrum Disorder - ASD) indica un ventaglio di diagnosi che vanno dalla Sindrome di Asperger all'autismo e che sono accumunate dalla presenza di sintomi comuni, che compromettono l'aspetto comportamentale e i rapporti con la società del soggetto. Al momento la diagnosi di ASD avviene affidandosi a test standardizzati riconosciuti eseguiti da personale medico specializzato. Negli ultimi anni si sono però generati diversi dataset di neuroimaging in cui vengono raccolte le immagini di risonanza magnetica provenienti da centri differenti e acquisite s

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Cifonelli, Antonio. "Probabilistic exponential smoothing for explainable AI in the supply chain domain." Electronic Thesis or Diss., Normandie, 2023. http://www.theses.fr/2023NORMIR41.

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Le rôle clé que l’IA pourrait jouer dans l’amélioration des activités commerciales est connu depuis longtemps, mais le processus de pénétration de cette nouvelle technologie a rencontré certains freins au sein des entreprises, en particulier, les coûts de mise œuvre. En moyenne, 2.8 ans sont nécessaires depuis la sélection du fournisseur jusqu’au déploiement complet d’une nouvelle solution. Trois points fondamentaux doivent être pris en compte lors du développement d’un nouveau modèle. Le désalignement des attentes, le besoin de compréhension et d’explications et les problèmes de performance e

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Guimbaud, Jean-Baptiste. "Enhancing Environmental Risk Scores with Informed Machine Learning and Explainable AI." Electronic Thesis or Diss., Lyon 1, 2024. http://www.theses.fr/2024LYO10188.

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Dès la conception, des facteurs environnementaux tels que la qualité de l'air ou les habitudes alimentaires peuvent significativement influencer le risque de développer diverses maladies chroniques. Dans la littérature épidémiologique, des indicateurs connus sous le nom de Scores de Risque Environnemental (Environmental Risk Score, ERS) sont utilisés non seulement pour identifier les individus à risque, mais aussi pour étudier les relations entre les facteurs environnementaux et la santé. Une limite de la plupart des ERSs est qu'ils sont exprimés sous forme de combinaisons linéaires d'un nombr

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Radulovic, Nedeljko. "Post-hoc Explainable AI for Black Box Models on Tabular Data." Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAT028.

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Les modèles d'intelligence artificielle (IA) actuels ont fait leurs preuves dans la résolution de diverses tâches, telles que la classification, la régression, le traitement du langage naturel (NLP) et le traitement d'images. Les ressources dont nous disposons aujourd'hui nous permettent d'entraîner des modèles d'IA très complexes pour résoudre différents problèmes dans presque tous les domaines : médecine, finance, justice, transport, prévisions, etc. Avec la popularité et l'utilisation généralisée des modèles d'IA, la nécessite d'assurer la confiance dans ces modèles s'est également accrue.

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Books on the topic "Explainable AI"

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Mishra, Pradeepta. Explainable AI Recipes. Apress, 2023. http://dx.doi.org/10.1007/978-1-4842-9029-3.

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Leofante, Francesco, and Matthew Wicker. Robust Explainable AI. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-89022-2.

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Gianfagna, Leonida, and Antonio Di Cecco. Explainable AI with Python. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68640-6.

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Holzinger, Andreas, Randy Goebel, Ruth Fong, Taesup Moon, Klaus-Robert Müller, and Wojciech Samek, eds. xxAI - Beyond Explainable AI. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04083-2.

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Sreedharan, Sarath, Anagha Kulkarni, and Subbarao Kambhampati. Explainable Human-AI Interaction. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-03767-2.

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Raval, Mehul S., Mohendra Roy, Tolga Kaya, and Rupal Kapdi. Explainable AI in Healthcare. Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003333425.

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

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Mishra, Pradeepta. Practical Explainable AI Using Python. Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-7158-2.

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Aluvalu, Rajanikanth, Mayuri Mehta, and Patrick Siarry, eds. Explainable AI in Health Informatics. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-3705-5.

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van Stein, Niki, and Anna V. Kononova, eds. Explainable AI for Evolutionary Computation. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-2540-6.

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Book chapters on the topic "Explainable AI"

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Geertsema, Paul. "Explainable AI." In Machine Learning for Managers. Routledge, 2023. http://dx.doi.org/10.4324/9781003330929-10.

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Pilon, Ricardo V. "Explainable AI." In Artificial Intelligence in Commercial Aviation. Routledge, 2023. http://dx.doi.org/10.4324/9781003018810-24.

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Santosh, KC, and Casey Wall. "EXplainable AI." In AI, Ethical Issues and Explainability—Applied Biometrics. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3935-8_2.

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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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Bialkova, Svetlana. "Explainable AI (XAI)." In The Rise of AI User Applications. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-56471-0_11.

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Xu, Shuyuan, Yingqiang Ge, and Yongfeng Zhang. "Causal Explainable AI." In Machine Learning for Causal Inference. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-35051-1_7.

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Ilievski, Filip. "Explainable Commonsense AI." In Synthesis Lectures on Computer Science. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-69974-0_2.

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Sardar, Tanvir Habib, Sunanda Das, and Bishwajeet Kumar Pandey. "Explainable AI (XAI)." In Medical Data Analysis and Processing using Explainable Artificial Intelligence. CRC Press, 2023. http://dx.doi.org/10.1201/9781003257721-1.

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Phillips, David. "Explainable AI (XAI)." In Augmenting Public Relations. CRC Press, 2024. http://dx.doi.org/10.1201/9781003507901-9.

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Mishra, Pradeepta. "Explainability for Ensemble Supervised Models." In Explainable AI Recipes. Apress, 2023. http://dx.doi.org/10.1007/978-1-4842-9029-3_4.

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Conference papers on the topic "Explainable AI"

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Zeiser, Tim, Dana Ehret, Theo Lutz, and Julian Saar. "Explainable AI in Manufacturing." In 2024 IEEE International Conference on Engineering, Technology, and Innovation (ICE/ITMC). IEEE, 2024. https://doi.org/10.1109/ice/itmc61926.2024.10794363.

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Kaur, Kirtpreet, Aarushi, and Zeba Afroz. "Applications of Explainable AI." In 2024 Second International Conference on Advanced Computing & Communication Technologies (ICACCTech). IEEE, 2024. https://doi.org/10.1109/icacctech65084.2024.00013.

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Benziane, Sarah, and Kaouter Labed. "Explainable AI for Biometrics." In 2024 International Conference on Applied Mathematics & Computer Science (ICAMCS). IEEE, 2024. https://doi.org/10.1109/icamcs62774.2024.00030.

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Kumar Pandiya, Dileep, Vilas Ramrao Joshi, and Kailash Nath Tripathi. "Demystifying AI Advances in Explainable AI (XAI)." In 2024 3rd International Conference on Computational Modelling, Simulation and Optimization (ICCMSO). IEEE, 2024. http://dx.doi.org/10.1109/iccmso61761.2024.00036.

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Coppari, Andrea, Luca Capra, and Cristiano Carlevaro. "XAINT: eXplainable AI through Iterative Network Truncation." In 2025 IEEE Symposium on Trustworthy, Explainable and Responsible Computational Intelligence (CITREx). IEEE, 2025. https://doi.org/10.1109/citrex64975.2025.10974946.

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Kamate, Sarvesh Prabhu, K. S. Venkatesh Prasad, and U. N. Ranjitha. "Comprehending Intelligent Systems with Explainable AI." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10724937.

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Umm-E-Habiba and Khan Mohammad Habibullah. "Explainable AI: A Diverse Stakeholder Perspective." In 2024 IEEE 32nd International Requirements Engineering Conference (RE). IEEE, 2024. http://dx.doi.org/10.1109/re59067.2024.00060.

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Pinto, Gabriel, Carlos Mello, and Ana Garcia. "Explainable AI in Labor Market Applications." In 17th International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and Technology Publications, 2025. https://doi.org/10.5220/0013384100003890.

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Rahman, Abdul, Shahab Saquib Sohail, Mohammad Sultan Alam, Anil Sharma, and Wathiq Mansoor. "Detecting Brain Cancer Using Explainable AI." In 2024 7th International Conference on Signal Processing and Information Security (ICSPIS). IEEE, 2024. https://doi.org/10.1109/icspis63676.2024.10812596.

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Bhuvaneswari, R., P. Kumar, and S. Kaviya. "Explainable AI-Driven Heart Disease Prediction." In 2025 International Conference on Visual Analytics and Data Visualization (ICVADV). IEEE, 2025. https://doi.org/10.1109/icvadv63329.2025.10961035.

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Reports on the topic "Explainable AI"

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Xhani, Donika. Ontology Driven Explainable AI for Tyre Engineering. University of Twente, 2023. http://dx.doi.org/10.3990/1.9789036558594.

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Pasupuleti, Murali Krishna. Decision Theory and Model-Based AI: Probabilistic Learning, Inference, and Explainability. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv525.

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Abstract Decision theory and model-based AI provide the foundation for probabilistic learning, optimal inference, and explainable decision-making, enabling AI systems to reason under uncertainty, optimize long-term outcomes, and provide interpretable predictions. This research explores Bayesian inference, probabilistic graphical models, reinforcement learning (RL), and causal inference, analyzing their role in AI-driven decision systems across various domains, including healthcare, finance, robotics, and autonomous systems. The study contrasts model-based and model-free approaches in decision-

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Bidkar, Deepti Vinayak. A literature review to underline necessity of explainability in AI and discuss existing explainable AI techniques. Iowa State University, 2021. http://dx.doi.org/10.31274/cc-20240624-50.

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Dong, Jiqian, Sikai Chen, and Samuel Labi. Promoting CAV Deployment by Enhancing the Perception Phase of the Autonomous Driving Using Explainable AI. Purdue University, 2023. http://dx.doi.org/10.5703/1288284317701.

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Otioma, Chuks, and Iain MacNeil. Robo Advisors as a Use Case of AI Systems: Linking Responsible Business Practices, Compliance and Outcomes. University of Glasgow and University of Strathclyde, 2025. https://doi.org/10.36399/gla.pubs.351605.

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In this paper, we explore the workings of robo-advisors as an example of AI-based systems. We discuss the performance and challenges of robo-advice, as well as offer reflections on how and why robo-advice as part of the broader fintech and financial services sector intersects practices in AI systems, regulation and compliance. We draw attention to the implications for explainable AI, the role of humans in the loop, compliance and business practices. Our approach focuses on how the AI capabilities in robo-advisors can help to build responsible business practices and compliance elements into ope

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Wang, Dali, Shih-Chieh Kao, and Daniel Ricciuto. Development of Explainable, Knowledge-Guided AI Models to Enhance the E3SM Land Model Development and Uncertainty Quantification. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1769696.

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Frieden, Jacob. Explainable AI: Extending the ConceptX Framework to the Exploration of Multilingual Latent Spaces in Source Code LLMs. Iowa State University, 2024. https://doi.org/10.31274/cc-20250502-38.

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Bakker, Craig. Multi-scale, Multi-disciplinary, and Multi-agent Explainable AI with Koopman-Undergirded Learning, Prediction, and Analysis (M3EA KULPA): Project Closeout Report. Office of Scientific and Technical Information (OSTI), 2025. https://doi.org/10.2172/2571228.

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Glandon, S. Ross, Casey L. Lorenzen, William F. Farthing, et al. Analysis tools and techniques for evaluating quality in synthetic data generated by the Virtual Autonomous Navigation Environment. US Army Engineer Research and Development Center, 2025. https://doi.org/10.21079/11681/49708.

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The capability to produce high-quality labeled synthetic image data is an important tool for building and maintaining machine learning datasets. However, ensuring computer-generated data is of high quality is very challenging. This report describes an effort to evaluate and improve synthetic image data generated by the Virtual Autonomous Navigation Environment’s Environment and Sensor Engine (VANE::ESE), as well as documenting a set of tools developed to process, analyze, and train models from, image datasets generated by VANE::ESE. Additionally, the results of several experiments are presente

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