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Keyword

model transparency

Description: Model transparency is the degree to which a model’s structure, assumptions, data lineage, and outputs can be inspected and understood in fields such as machine learning and systems modeling.
OpenAlex ID: keywords/model-transparency
Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
2019 · Ramprasaath R. Selvaraju, Michael Cogswell, et al. · International Journal of Computer Vision · 6,858
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Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
2018 · Amina Adadi, Mohammed Berrada · IEEE Access · 6,262
Model Cards for Model Reporting
2019 · Margaret Mitchell, Simone Wu, et al. · 2,632
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Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence
2023 · Vikas Hassija, Vinay Chamola, et al. · Cognitive Computation · 2,098
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Techniques for interpretable machine learning
2019 · Mengnan Du, Ninghao Liu, et al. · Communications of the ACM · 1,256
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Explainable artificial intelligence: A survey
2018 · Filip Karlo Dosilovic, Mario Brcic, et al. · International Convention on Information and Communication Technology, Electronics and Microelectronics · 1,147
A Survey on the Explainability of Supervised Machine Learning
2021 · Nadia Burkart, Marco F. Huber · Journal of Artificial Intelligence Research · 1,002
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Explainable artificial intelligence: a comprehensive review
2021 · Dang Minh, H. Xiang Wang, et al. · Artificial Intelligence Review · 978
A Perspective on Explainable Artificial Intelligence Methods: SHAP and LIME
2024 · Ahmed M. Salih, Zahra Raisi‐Estabragh, et al. · Advanced Intelligent Systems · 882
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Model Transparency and Validation
2012 · David M. Eddy, William Hollingworth, et al. · Medical Decision Making · 801
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A review of modelling tools for energy and electricity systems with large shares of variable renewables
2018 · Hans-Kristian Ringkjøb, Peter M. Haugan, et al. · Renewable and Sustainable Energy Reviews · 761
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Manipulating and Measuring Model Interpretability
2021 · Forough Poursabzi-Sangdeh, Daniel G Goldstein, et al. · CHI Conference on Human Factors in Computing Systems · 708
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Explainability for Large Language Models: A Survey
2024 · Haiyan Zhao, Hanjie Chen, et al. · ACM Transactions on Intelligent Systems and Technology · 675
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Good practice in Bayesian network modelling
2012 · Serena H. Chen, Carmel A. Pollino · Environmental Modelling & Software · 660
Explainable machine learning in deployment
2020 · Umang Bhatt, Alice Xiang, et al. · 631
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A Survey of Methods for Explaining Black Box Models
2018 · Riccardo Guidotti, Anna Monreale, et al. · ACM Computing Surveys · 630
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Approximating XGBoost with an interpretable decision tree
2021 · Omer Sagi, Lior Rokach · Information Sciences · 560
Describing human decisions in agent-based models – ODD + D, an extension of the ODD protocol
2013 · Birgit Müller, Friedrich Bohn, et al. · Environmental Modelling & Software · 544
Revealing the Dark Secrets of BERT
2019 · Olga Kovaleva, Alexey Romanov, et al. · 513
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Holistic Evaluation of Language Models
2023 · Rishi Bommasani, Percy Liang, et al. · Annals of the New York Academy of Sciences · 476
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Model Transparency and Validation: A Report of the ISPOR-SMDM Modeling Good Research Practices Task Force-7
2012 · David M. Eddy, William Hollingworth, et al. · Value in Health · 465
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Interpretability of deep learning models: A survey of results
2017 · Supriyo Chakraborty, Richard Tomsett, et al. · 460
Ecological models supporting environmental decision making: a strategy for the future
2010 · Amelie Schmolke, Pernille Thorbek, et al. · Trends in Ecology & Evolution · 460
MINIMAR (MINimum Information for Medical AI Reporting): Developing reporting standards for artificial intelligence in health care
2020 · Tina Hernandez-Boussard, Selen Bozkurt, et al. · Journal of the American Medical Informatics Association · 406
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An artificial immune system for data analysis
2000 · Jon Timmis, Mark Neal, et al. · Biosystems · 398
Works count: 3,897
Citation count: 72,340
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