Journal articles on the topic 'Interpretable AI'
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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.
Full textJia, 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.
Full textXu, 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.
Full textSkirzyński, 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.
Full textTomsett, 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.
Full textHerzog, 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.
Full textSchmidt 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.
Full textPark, 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.
Full textBaşağaoğlu, 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.
Full textDemajo, 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.
Full textGhoshRoy, Debasmita, Parvez Ahmad Alvi, and KC Santosh. "Explainable AI to Predict Male Fertility Using Extreme Gradient Boosting Algorithm with SMOTE." Electronics 12, no. 1 (December 21, 2022): 15. http://dx.doi.org/10.3390/electronics12010015.
Full textDikshit, Abhirup, and Biswajeet Pradhan. "Interpretable and explainable AI (XAI) model for spatial drought prediction." Science of The Total Environment 801 (December 2021): 149797. http://dx.doi.org/10.1016/j.scitotenv.2021.149797.
Full textRampal, Neelesh, Tom Shand, Adam Wooler, and Christo Rautenbach. "Interpretable Deep Learning Applied to Rip Current Detection and Localization." Remote Sensing 14, no. 23 (November 29, 2022): 6048. http://dx.doi.org/10.3390/rs14236048.
Full textSolanke, Abiodun A. "Explainable digital forensics AI: Towards mitigating distrust in AI-based digital forensics analysis using interpretable models." Forensic Science International: Digital Investigation 42 (July 2022): 301403. http://dx.doi.org/10.1016/j.fsidi.2022.301403.
Full textEder, Matthias, Emanuel Moser, Andreas Holzinger, Claire Jean-Quartier, and Fleur Jeanquartier. "Interpretable Machine Learning with Brain Image and Survival Data." BioMedInformatics 2, no. 3 (September 6, 2022): 492–510. http://dx.doi.org/10.3390/biomedinformatics2030031.
Full textVishwarupe, Varad, Prachi M. Joshi, Nicole Mathias, Shrey Maheshwari, Shweta Mhaisalkar, and Vishal Pawar. "Explainable AI and Interpretable Machine Learning: A Case Study in Perspective." Procedia Computer Science 204 (2022): 869–76. http://dx.doi.org/10.1016/j.procs.2022.08.105.
Full textCombs, Kara, Mary Fendley, and Trevor Bihl. "A Preliminary Look at Heuristic Analysis for Assessing Artificial Intelligence Explainability." WSEAS TRANSACTIONS ON COMPUTER RESEARCH 8 (June 1, 2020): 61–72. http://dx.doi.org/10.37394/232018.2020.8.9.
Full textBelle, Vaishak. "The quest for interpretable and responsible artificial intelligence." Biochemist 41, no. 5 (October 18, 2019): 16–19. http://dx.doi.org/10.1042/bio04105016.
Full textCalegari, Roberta, Giovanni Ciatto, and Andrea Omicini. "On the integration of symbolic and sub-symbolic techniques for XAI: A survey." Intelligenza Artificiale 14, no. 1 (September 17, 2020): 7–32. http://dx.doi.org/10.3233/ia-190036.
Full textVasan Srinivasan, Aditya, and Mona de Boer. "Improving trust in data and algorithms in the medium of AI." Maandblad Voor Accountancy en Bedrijfseconomie 94, no. 3/4 (April 22, 2020): 147–60. http://dx.doi.org/10.5117/mab.94.49425.
Full textAlm, Cecilia O., and Alex Hedges. "Visualizing NLP in Undergraduate Students' Learning about Natural Language." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 17 (May 18, 2021): 15480–88. http://dx.doi.org/10.1609/aaai.v35i17.17822.
Full textBaldini, Ioana, Clark Barrett, Antonio Chella, Carlos Cinelli, David Gamez, Leilani Gilpin, Knut Hinkelmann, et al. "Reports of the AAAI 2019 Spring Symposium Series." AI Magazine 40, no. 3 (September 30, 2019): 59–66. http://dx.doi.org/10.1609/aimag.v40i3.5181.
Full textHah, Hyeyoung, and Deana Goldin. "Moving toward AI-assisted decision-making: Observation on clinicians’ management of multimedia patient information in synchronous and asynchronous telehealth contexts." Health Informatics Journal 28, no. 1 (January 2022): 146045822210770. http://dx.doi.org/10.1177/14604582221077049.
Full textGómez, Blas, Estefanía Coronado, José Villalón, and Antonio Garrido. "Intelli-GATS: Dynamic Selection of the Wi-Fi Multicast Transmission Policy Using Interpretable-AI." Wireless Communications and Mobile Computing 2022 (November 30, 2022): 1–18. http://dx.doi.org/10.1155/2022/7922273.
Full textWeitz, Katharina, Teena Hassan, Ute Schmid, and Jens-Uwe Garbas. "Deep-learned faces of pain and emotions: Elucidating the differences of facial expressions with the help of explainable AI methods." tm - Technisches Messen 86, no. 7-8 (July 26, 2019): 404–12. http://dx.doi.org/10.1515/teme-2019-0024.
Full textFuhrman, Jordan D., Naveena Gorre, Qiyuan Hu, Hui Li, Issam El Naqa, and Maryellen L. Giger. "A review of explainable and interpretable AI with applications in COVID‐19 imaging." Medical Physics 49, no. 1 (December 7, 2021): 1–14. http://dx.doi.org/10.1002/mp.15359.
Full textSantala, Onni E., Jukka A. Lipponen, Helena Jäntti, Tuomas T. Rissanen, Mika P. Tarvainen, Tomi P. Laitinen, Tiina M. Laitinen, et al. "Continuous mHealth Patch Monitoring for the Algorithm-Based Detection of Atrial Fibrillation: Feasibility and Diagnostic Accuracy Study." JMIR Cardio 6, no. 1 (June 21, 2022): e31230. http://dx.doi.org/10.2196/31230.
Full textCavallaro, Massimo, Ed Moran, Benjamin Collyer, Noel D. McCarthy, Christopher Green, and Matt J. Keeling. "Informing antimicrobial stewardship with explainable AI." PLOS Digital Health 2, no. 1 (January 5, 2023): e0000162. http://dx.doi.org/10.1371/journal.pdig.0000162.
Full textHijazi, Haytham, Manar Abu Talib, Ahmad Hasasneh, Ali Bou Nassif, Nafisa Ahmed, and Qassim Nasir. "Wearable Devices, Smartphones, and Interpretable Artificial Intelligence in Combating COVID-19." Sensors 21, no. 24 (December 17, 2021): 8424. http://dx.doi.org/10.3390/s21248424.
Full textDe Sousa Ribeiro, Manuel, and João Leite. "Aligning Artificial Neural Networks and Ontologies towards Explainable AI." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 6 (May 18, 2021): 4932–40. http://dx.doi.org/10.1609/aaai.v35i6.16626.
Full textConnie, Tee, Yee Fan Tan, Michael Kah Ong Goh, Hock Woon Hon, Zulaikha Kadim, and Li Pei Wong. "Explainable health prediction from facial features with transfer learning." Journal of Intelligent & Fuzzy Systems 42, no. 3 (February 2, 2022): 2491–503. http://dx.doi.org/10.3233/jifs-211737.
Full textYao, Melissa Min-Szu, Hao Du, Mikael Hartman, Wing P. Chan, and Mengling Feng. "End-to-End Calcification Distribution Pattern Recognition for Mammograms: An Interpretable Approach with GNN." Diagnostics 12, no. 6 (June 2, 2022): 1376. http://dx.doi.org/10.3390/diagnostics12061376.
Full textNguyen Thu Hien, Nguyen Phuong Nhung, and Nguyen Tuan Linh. "Adaptive neuro-fuzzy inference system classifier with interpretability for cancer diagnostic." Journal of Military Science and Technology, CSCE6 (December 30, 2022): 56–64. http://dx.doi.org/10.54939/1859-1043.j.mst.csce6.2022.56-64.
Full textDas, Arun, Jeffrey Mock, Yufei Huang, Edward Golob, and Peyman Najafirad. "Interpretable Self-Supervised Facial Micro-Expression Learning to Predict Cognitive State and Neurological Disorders." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 1 (May 18, 2021): 818–26. http://dx.doi.org/10.1609/aaai.v35i1.16164.
Full textDe, Tanusree, Prasenjit Giri, Ahmeduvesh Mevawala, Ramyasri Nemani, and Arati Deo. "Explainable AI: A Hybrid Approach to Generate Human-Interpretable Explanation for Deep Learning Prediction." Procedia Computer Science 168 (2020): 40–48. http://dx.doi.org/10.1016/j.procs.2020.02.255.
Full textChao, Wenhan, Xin Jiang, Zhunchen Luo, Yakun Hu, and Wenjia Ma. "Interpretable Charge Prediction for Criminal Cases with Dynamic Rationale Attention." Journal of Artificial Intelligence Research 66 (November 25, 2019): 743–64. http://dx.doi.org/10.1613/jair.1.11377.
Full textQadir, Junaid, Mohammad Qamar Islam, and Ala Al-Fuqaha. "Toward accountable human-centered AI: rationale and promising directions." Journal of Information, Communication and Ethics in Society 20, no. 2 (February 10, 2022): 329–42. http://dx.doi.org/10.1108/jices-06-2021-0059.
Full textCervera-Lierta, Alba, Mario Krenn, and Alán Aspuru-Guzik. "Design of quantum optical experiments with logic artificial intelligence." Quantum 6 (October 13, 2022): 836. http://dx.doi.org/10.22331/q-2022-10-13-836.
Full textKitamura, Shinji, Kensaku Takahashi, Yizhen Sang, Kazuhiko Fukushima, Kenji Tsuji, and Jun Wada. "Deep Learning Could Diagnose Diabetic Nephropathy with Renal Pathological Immunofluorescent Images." Diagnostics 10, no. 7 (July 9, 2020): 466. http://dx.doi.org/10.3390/diagnostics10070466.
Full textMakridis, Christos, Seth Hurley, Mary Klote, and Gil Alterovitz. "Ethical Applications of Artificial Intelligence: Evidence From Health Research on Veterans." JMIR Medical Informatics 9, no. 6 (June 2, 2021): e28921. http://dx.doi.org/10.2196/28921.
Full textRoopaei*, Mehdi, Hunter Durian, and Joey Godiska. "Explainable AI in Internet of Control System Distributed at Edge-Cloud Architecture." International Journal of Engineering and Advanced Technology 10, no. 3 (February 28, 2021): 136–42. http://dx.doi.org/10.35940/ijeat.c2246.0210321.
Full textMartens, Harald. "Interpretable machine learning with an eye for the physics: Hyperspectral Vis/NIR “video” of drying wood analyzed by hybrid subspace modeling." NIR news 32, no. 7-8 (November 25, 2021): 24–32. http://dx.doi.org/10.1177/09603360211062706.
Full textMarten, Dennis, Carsten Hilgenfeld, and Andreas Heuer. "Scalable In-Database Machine Learning for the Prediction of Port-to-Port Routes." Journal für Mobilität und Verkehr, no. 6 (November 10, 2020): 2–10. http://dx.doi.org/10.34647/jmv.nr6.id42.
Full textZhang, Quan, Qian Du, and Guohua Liu. "A whole-process interpretable and multi-modal deep reinforcement learning for diagnosis and analysis of Alzheimer’s disease ∗." Journal of Neural Engineering 18, no. 6 (December 1, 2021): 066032. http://dx.doi.org/10.1088/1741-2552/ac37cc.
Full textSucipto, Kathleen, Archit Khosla, Michael Drage, Yilan Wang, Darren Fahy, Mary Lin, Murray Resnick, et al. "QUANTITATIVE AND EXPLAINABLE ARTIFICIAL INTELLIGENCE (AI)-POWERED APPROACHES TO PREDICT ULCERATIVE COLITIS DISEASE ACTIVITY FROM HEMATOXYLIN AND EOSIN (H&E)-STAINED WHOLE SLIDE IMAGES (WSI)." Inflammatory Bowel Diseases 29, Supplement_1 (January 26, 2023): S22—S23. http://dx.doi.org/10.1093/ibd/izac247.042.
Full textSilva, Vivian S., André Freitas, and Siegfried Handschuh. "Exploring Knowledge Graphs in an Interpretable Composite Approach for Text Entailment." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 7023–30. http://dx.doi.org/10.1609/aaai.v33i01.33017023.
Full textWang, Yue, and Sai Ho Chung. "Artificial intelligence in safety-critical systems: a systematic review." Industrial Management & Data Systems 122, no. 2 (December 7, 2021): 442–70. http://dx.doi.org/10.1108/imds-07-2021-0419.
Full textThrun, Michael C., Alfred Ultsch, and Lutz Breuer. "Explainable AI Framework for Multivariate Hydrochemical Time Series." Machine Learning and Knowledge Extraction 3, no. 1 (February 4, 2021): 170–204. http://dx.doi.org/10.3390/make3010009.
Full textNeto, Pedro C., Sara P. Oliveira, Diana Montezuma, João Fraga, Ana Monteiro, Liliana Ribeiro, Sofia Gonçalves, Isabel M. Pinto, and Jaime S. Cardoso. "iMIL4PATH: A Semi-Supervised Interpretable Approach for Colorectal Whole-Slide Images." Cancers 14, no. 10 (May 18, 2022): 2489. http://dx.doi.org/10.3390/cancers14102489.
Full textHaupt, Sue Ellen, William Chapman, Samantha V. Adams, Charlie Kirkwood, J. Scott Hosking, Niall H. Robinson, Sebastian Lerch, and Aneesh C. Subramanian. "Towards implementing artificial intelligence post-processing in weather and climate: proposed actions from the Oxford 2019 workshop." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 379, no. 2194 (February 15, 2021): 20200091. http://dx.doi.org/10.1098/rsta.2020.0091.
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