检索结果-内蒙古大学图书馆

arXiv 2021年

作者： Zhang, Jin Feng, Fan Marti-Puig, Pere Caiafa, Cesar F. Sun, Zhe Duan, Feng Solé-Casals, Jordi College of Computer Science Nankai University Tianjin300071 China College of Artificial Intelligence Nankai University Tianjin300350 China Data and Signal Processing Group University of Vic—Central University of Catalonia Catalonia Vic08500 Spain Instituto Argentino de Radioastronomía CONICET CCT La Plata CIC-PBA UNLP V. Elisa1894 Argentina Tensor Learning Team Center for Advanced Intelligence Project RIKEN Tokyo103-0027 Japan Computational Engineering Applications Unit Head Office for Information Systems and Cybersecurity RIKEN Saitama351-0198 Japan

Empirical mode decomposition (EMD) has developed into a prominent tool for adaptive, scale-based signal analysis in various fields like robotics, security and biomedical engineering. Since the dramatic increase in amount of data puts forward higher requirements for the capability of real-time signal analysis, it is difficult for existing EMD and its variants to trade off the growth of data dimension and the speed of signal analysis. In order to decompose multi-dimensional signals at a faster speed, we present a novel signal-serialization method (serial-EMD), which concatenates multi-variate or multi-dimensional signals into a one-dimensional signal and uses various one-dimensional EMD algorithms to decompose it. To verify the effects of the proposed method, synthetic multi-variate time series, artificial 2D images with various textures and real-world facial images are tested. Compared with existing multi-EMD algorithms, the decomposition time becomes significantly reduced. In addition, the results of facial recognition with Intrinsic Mode Functions (IMFs) extracted using our method can achieve a higher accuracy than those obtained by existing multi-EMD algorithms, which demonstrates the superior performance of our method in terms of the quality of IMFs. Furthermore, this method can provide a new perspective to optimize the existing EMD algorithms, that is, transforming the structure of the input signal rather than being constrained by developing envelope computation techniques or signal decomposition methods. In summary, the study suggests that the serial-EMD technique is a highly competitive and fast alternative for multi-dimensional signal analysis. Copyright © 2021, The Authors. All rights reserved.

关键词： Signal analysis

来源：评论

学校读者我要写书评

暂无评论

Cluster analysis methods and future time perspective groups of second-year engineering students in a major-required course 125

Cluster analysis methods and future time perspective groups ...

引用

125th ASEE Annual Conference and Exposition

作者： Chasmar, Justine Ehlert, Katherine M. Center for Data Mathematical and Computational Sciences Quantitative Reasoning Center Goucher College United States Engineering and Science Education Department College of Engineering Computing and Applied Sciences Clemson University United States

来源：评论

学校读者我要写书评

暂无评论

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

arXiv

引用

arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

来源：评论

学校读者我要写书评

暂无评论

Distillation ≈ early stopping? harvesting dark knowledge utilizing anisotropic information retrieval for overparameterized neural network

arXiv

引用

arXiv 2019年

作者： Dong, Bin Hou, Jikai Lu, Yiping Zhang, Zhihua Beijing International Center for Mathematical Research Peking University Center for Data Science Peking University Beijing Institute of Big Data Research Beijing China School of Mathematical Sciences Peking University Beijing China Institute for Computational & Mathematical Engineering Stanford University

Distillation is a method to transfer knowledge from one model to another and often achieves higher accuracy with the same capacity. In this paper, we aim to provide a theoretical understanding on what mainly helps with the distillation. Our answer is "early stopping". Assuming that the teacher network is overparameterized, we argue that the teacher network is essentially harvesting dark knowledge from the data via early stopping. This can be justified by a new concept, Anisotropic Information Retrieval (AIR), which means that the neural network tends to fit the informative information first and the non-informative information (including noise) later. Motivated by the recent development on theoretically analyzing overparameterized neural networks, we can characterize AIR by the eigenspace of the Neural Tangent Kernel(NTK). AIR facilities a new understanding of distillation. With that, we further utilize distillation to refine noisy labels. We propose a self-distillation algorithm to sequentially distill knowledge from the network in the previous training epoch to avoid memorizing the wrong labels. We also demonstrate, both theoretically and empirically, that self-distillation can benefit from more than just early stopping. Theoretically, we prove convergence of the proposed algorithm to the ground truth labels for randomly initialized overparameterized neural networks in terms of `2 distance, while the previous result was on convergence in 0-1 loss. The theoretical result ensures the learned neural network enjoy a margin on the training data which leads to better generalization. Empirically, we achieve better testing accuracy and entirely avoid early stopping which makes the algorithm more user-friendly. Copyright © 2019, The Authors. All rights reserved.

关键词： Anisotropy

来源：评论

学校读者我要写书评

暂无评论

Chimera states in hybrid coupled neuron populations

arXiv

引用

arXiv 2020年

作者： Calim, Ali Torres, Joaquin J. Ozer, Mahmut Uzuntarla, Muhammet Department of Biomedical Engineering Zonguldak Bulent Ecevit University Zonguldak Turkey Department of Electromagnetism and Physics of the Matter Institute Carlos I for Theoretical and Computational Physics University of Granada GranadaE-18071 Spain Ministry of National Education Ankara Turkey Center for Artificial Intelligence and Data Science Istanbul Technical University Istanbul Turkey Department of Electrical and Electronics Engineering Zonguldak Bulent Ecevit University Zonguldak Turkey

Here we study the emergence of chimera states, a recently reported phenomenon referring to the coexistence of synchronized and unsynchronized dynamical units, in a population of Morris-Lecar neurons which are coupled by both electrical and chemical synapses, constituting a hybrid synaptic architecture, as in actual brain connectivity. This scheme consists of a nonlocal network where the nearest neighbor neurons are coupled by electrical synapses, while the synapses from more distant neurons are of the chemical type. We demonstrate that peculiar dynamical behaviors, including chimera state and traveling wave, exist in such a hybrid coupled neural system, and analyze how the relative abundance of chemical and electrical synapses affects the features of chimera and different synchrony states (i.e. incoherent, traveling wave and coherent) and the regions in the space of relevant parameters for their emergence. Additionally, we show that, when the relative population of chemical synapses increases further, a new intriguing chaotic dynamical behavior appears above the region for chimera states. This is characterized by the coexistence of two distinct synchronized states with different amplitude, and an unsynchronized state, that we denote as a chaotic amplitude chimera. We also discuss about the computational implications of such state. Copyright © 2020, The Authors. All rights reserved.

关键词： Neurons

来源：评论

学校读者我要写书评

暂无评论

What affects the performance of POD for the simulation of heat transfer through building component?

引用

Journal of Physics: Conference Series 2021年第1期2069卷

作者： Tianfeng Hou Staf Roels Hans Janssen KU Leuven Department of Civil Engineering Building Physics Section 3001 Leuven Belgium Data Assimilation Research Team RIKEN Center for Computational Science Kobe Japan RIKEN Prediction Science Laboratory Kobe Japan RIKEN Interdisciplinary Theoretical and Mathematical Sciences Program Kobe Japan

The capability of the proper orthogonal decomposition for the simulation of heat transfer in building components is investigated via three applications: linear heat transfer (not coupled to mass transfer), mildly non-linear heat transfer (coupled to air and moisture transfer (hygroscopic)) and highly non-linear heat transfer (coupled to moisture transfer (capillary)). It is shown that increasing non-linearity leads to an increasing number of required construction modes. To further investigate the reason for this degrading performance of POD, the singular values' decay progress from the different training snapshots is addressed in this paper. The results confirm that a fast decay of the singular values implies a high interrelation of the snapshots and a better performance of the POD method.

关键词：

来源：评论

学校读者我要写书评

暂无评论

An extreme test case for planet formation: a close-in Neptune orbiting an ultracool star

arXiv

引用

arXiv 2023年

作者： Stefánsson, Guðmundur Mahadevan, Suvrath Miguel, Yamila Robertson, Paul Delamer, Megan Kanodia, Shubham Cañas, Caleb Winn, Joshua Ninan, Joe Terrien, Ryan Holcomb, Rae Ford, Eric Zawadzki, Brianna Bowler, Brendan P. Bender, Chad Cochran, William Diddams, Scott Endl, Michael Fredrick, Connor Halverson, Samuel Hearty, Fred Hill, Gary J. Lin, Andrea Metcalf, Andrew Monson, Andrew Ramsey, Lawrence Roy, Arpita Schwab, Christian Wright, Jason Zeimann, Gregory Princeton University Department of Astrophysical Sciences United States Department of Astronomy & Astrophysics The Pennsylvania State University United States Center for Exoplanets and Habitable Worlds The Pennsylvania State University United States ETH Zurich Institute for Particle Physics & Astrophysics Switzerland Leiden Observatory University of Leiden Netherlands SRON Netherlands Institute for Space Research Netherlands Department of Physics & Astronomy University of California Irvine United States Earth and Planets Laboratory Carnegie Institution for Science United States NASA Goddard Space Flight Center United States Department of Astronomy & Astrophysics Tata Institute of Fundamental Research India Carleton College United States Center for Astrostatistics The Pennsylvania State University United States Institute for Computational & Data Sciences The Pennsylvania State University United States Department of Astronomy The University of Texas at Austin AustinTX78712 United States Steward Observatory The University of Arizona United States Center for Planetary Systems Habitability and McDonald Observatory UT Austin United States Electrical Computer and Energy Engineering University of Colorado United States National Institute of Standards & Technology United States Department of Physics University of Colorado United States Jet Propulsion Laboratory California Institute of Technology United States McDonald Observatory UT Austin United States Space Vehicles Directorate Air Force Research Laboratory United States Space Telescope Science Institute United States Department of Physics and Astronomy Johns Hopkins University United States School of Mathematical and Physical Sciences Macquarie University Australia Penn State Extraterrestrial Intelligence Center The Pennsylvania State University United States Hobby-Eberly Telescope University of Texas Austin AustinTX78712 United States

In current theories of planet formation, close-orbiting planets as massive as Neptune are expected to be very rare around low-mass stars. We report the discovery of a Neptune-mass planet orbiting the 'ultracool' star LHS 3154, which is nine times less massive than the Sun. The planet's orbital period is 3.7 days and its minimum mass is 13.2 Earth masses, giving it the largest known planet-to-star mass ratio among short-period planets ( Copyright © 2023, The Authors. All rights reserved.

关键词： Planets

来源：评论

学校读者我要写书评

暂无评论

THE TEMPO SURVEY II: science CASES LEVERAGED FROM A PROPOSED 30-DAY TIME DOMAIN SURVEY OF THE ORION NEBULA WITH THE NANCY GRACE ROMAN SPACE TELESCOPE

arXiv

引用

arXiv 2024年

作者： Soares-Furtado, Melinda Limbach, Mary Anne Vanderburg, Andrew Bally, John Becker, Juliette Rosen, Anna L. Bouma, Luke G. Vos, Johanna M. Howell, Steve B. Beatty, Thomas G. Best, William M.J. Cody, Anne Marie Distler, Adam D’Onghia, Elena Heller, René Hensley, Brandon S. Hinkel, Natalie R. Jackson, Brian Kounkel, Marina Kraus, Adam Mann, Andrew W. Marston, Nicholas T. Robberto, Massimo Rodriguez, Joseph E. Steffen, Jason H. Teske, Johanna K. Townsend, Richard Yarza, Ricardo Youngblood, Allison Department of Astronomy University of Wisconsin-Madison 475 N. Charter St. MadisonWI53706 United States Department of Physics Kavli Institute for Astrophysics and Space Research Massachusetts Institute of Technology CambridgeMA02139 United States Department of Astronomy University of Michigan Ann ArborMI48109 United States Center for Astrophysics and Space Astronomy Astrophysical and Planetary Sciences Department University of Colorado BoulderCO80389 United States Department of Astronomy San Diego State University San DiegoCA92182 United States Computational Science Research Center San Diego State University San DiegoCA92182 United States Department of Astronomy MC 249-17 California Institute of Technology PasadenaCA91125 United States School of Physics Trinity College Dublin The University of Dublin Dublin 2 Ireland NASA Ames Research Center Moffett FieldCA94035 United States Department of Astronomy The University of Texas at Austin AustinTX78712 United States SETI Institute 189 N. Bernardo Ave #200 Mountain ViewCA94043 United States Max Planck Institute for Solar System Research Justus-von-Liebig-Weg 3 Göttingen37077 Germany Jet Propulsion Laboratory California Institute of Technology 4800 Oak Grove Drive PasadenaCA91109 United States Physics & Astronomy Department Louisiana State University Baton RougeLA70803 United States Department of Physics Boise State University BoiseID83725-1570 United States Department of Physics & Astronomy Vanderbilt University NashvilleTN37235 United States Department of Physics & Astronomy The University of North Carolina at Chapel Hill Chapel HillNC27599-3255 United States Space Telescope Science Institute 3700 San Martin Drive BaltimoreMD21218 United States Johns Hopkins University 3400 N. Charles St. BaltimoreMD21218 United States Center for Data Intensive and Time Domain Astronomy Department of Physics and Astronomy Michigan State University East LansingMI48824 United States Un

The TEMPO (Transiting Exosatellites, Moons, and Planets in Orion) Survey is a proposed 30-day observational campaign using the Nancy Grace Roman Space Telescope. By providing deep, high-resolution, short-cadence infrared photometry of a dynamic star-forming region, TEMPO will investigate the demographics of exosatellites orbiting free-floating planets and brown dwarfs — a largely unexplored discovery space. Here, we present the simulated detection yields of three populations: extrasolar moon analogs orbiting free-floating planets, exosatellites orbiting brown dwarfs, and exoplanets orbiting young stars. Additionally, we outline a comprehensive range of anticipated scientific outcomes accompanying such a survey. These science drivers include: obtaining observational constraints to test prevailing theories of moon, planet, and star formation;directly detecting widely separated exoplanets orbiting young stars;investigating the variability of young stars and brown dwarfs;constraining the low-mass end of the stellar initial mass function;constructing the distribution of dust in the Orion Nebula and mapping evolution in the near-infrared extinction law;mapping emission features that trace the shocked gas in the region;constructing a dynamical map of Orion members using proper motions;and searching for extragalactic sources and transients via deep extragalactic observations reaching a limiting magnitude of mAB = 29.7 mag (F146 filter). © 2024, CC BY.

关键词： Orbits

来源：评论

学校读者我要写书评

暂无评论

Scaling up the lattice dynamics of amorphous materials by orders of magnitude

arXiv

引用

arXiv 2020年

作者： Kriuchevskyi, Ivan Palyulin, Vladimir V. Milkus, Rico Elder, Robert M. Sirk, Timothy W. Zaccone, Alessio Department of Physics "A. Pontremoli" University of Milan via Celoria 16 Milan20133 Italy Centre for Computational and Data-Intensive Science and Engineering Skolkovo Institute of Science and Technology Nobelya Ulitsa 3 Moscow121205 Russia Department of Chemical Engineering and Biotechnology University of Cambridge CambridgeCB3 0AS United Kingdom Polymers Branch U.S. Army Research Laboratory Aberdeen Proving Ground MD United States Bennett Aerospace Inc. CaryNC27518 United States Center for Devices and Radiological Health U.S. Food and Drug Administration Silver SpringMD20903 United States

We generalise the non-affine theory of viscoelasticity for use with large, well-sampled systems of arbitrary chemical complexity. Having in mind predictions of mechanical and vibrational properties of amorphous systems with atomistic resolution, we propose an extension of the Kernel Polynomial Method (KPM) for the computation of the vibrational density of states (VDOS) and the eigenmodes, including the Γ-correlator of the affine force-field, which is a key ingredient of lattice-dynamic calculations of viscoelasticity. We show that the results converge well to the solution obtained by direct diagonalization (DD) of the Hessian (dynamical) matrix. As is well known, the DD approach has prohibitively high computational requirements for systems with N = 104 atoms or larger. Instead, the KPM approach developed here allows one to scale up lattice dynamic calculations of real materials up to 106 atoms, with a hugely more favorable (linear) scaling of computation time and memory consumption with N. Copyright © 2020, The Authors. All rights reserved.

关键词： Viscoelasticity

来源：评论

学校读者我要写书评

暂无评论

Lessons Learned from Assessing Trustworthy AI in Practice

引用

Digital Society 2023年第3期2卷 1-25页

作者： Vetter, Dennis Amann, Julia Bruneault, Frédérick Coffee, Megan Düdder, Boris Gallucci, Alessio Gilbert, Thomas Krendl Hagendorff, Thilo van Halem, Irmhild Hickman, Eleanore Hildt, Elisabeth Holm, Sune Kararigas, Georgios Kringen, Pedro Madai, Vince I. Wiinblad Mathez, Emilie Tithi, Jesmin Jahan Westerlund, Magnus Wurth, Renee Zicari, Roberto V. Computational Vision and Artificial Intelligence Lab Goethe University Frankfurt Frankfurt Am Main Germany Z-Inspection® Initiative Venice Italy Health Ethics and Policy Lab ETH Zurich Zurich Switzerland Strategy and Innovation Careum Foundation Zurich Switzerland Philosophie Departement Collège André-Laurendeau Montréal Canada École Des Médias Université du Québec À Montréal Montréal Canada Department of Medicine Division of Infectious Diseases and Immunology New York University Grossman School of Medicine New York City USA Department of Computer Science University of Copenhagen Copenhagen Denmark Digital Life Initiative Cornell Tech New York City USA Cluster of Excellence “Machine Learning: New Perspectives for Science” University of Tuebingen Tuebingen Germany School of Law University of Bristol Bristol UK Center for the Study of Ethics in the Professions Illinois Institute of Technology Chicago USA Department of Business Management and Analytics Arcada University of Applied Sciences Helsinki Finland Department of Food & Resource Economics University of Copenhagen Copenhagen Denmark Department of Physiology Faculty of Medicine University of Iceland Reykjavik Iceland QUEST Centre for Responsible Research Berlin Institute of Health Charité Universitätsmedizin Berlin Berlin Germany Faculty of Computing Engineering and the Built Environment School of Computing and Digital Technology Birmingham City University Birmingham UK Parallel Computing Labs Intel Santa Clara USA School of Economics Innovation and Technology Kristiania University College Oslo Norway Data Science Graduate School Seoul National University Seoul South Korea

Building artificial intelligence (AI) systems that adhere to ethical standards is a complex problem. Even though a multitude of guidelines for the design and development of such trustworthy AI systems exist, these guidelines focus on high-level and abstract requirements for AI systems, and it is often very difficult to assess if a specific system fulfills these requirements. The Z-Inspection® process provides a holistic and dynamic framework to evaluate the trustworthiness of specific AI systems at different stages of the AI lifecycle, including intended use, design, and development. It focuses, in particular, on the discussion and identification of ethical issues and tensions through the analysis of socio-technical scenarios and a requirement-based framework for ethical and trustworthy AI. This article is a methodological reflection on the Z-Inspection® process. We illustrate how high-level guidelines for ethical and trustworthy AI can be applied in practice and provide insights for both AI researchers and AI practitioners. We share the lessons learned from conducting a series of independent assessments to evaluate the trustworthiness of real-world AI systems, as well as key recommendations and practical suggestions on how to ensure a rigorous trustworthiness assessment throughout the lifecycle of an AI system. The results presented in this article are based on our assessments of AI systems in the healthcare sector and environmental monitoring, where we used the framework for trustworthy AI proposed in the Ethics Guidelines for Trustworthy AI by the European Commission’s High-Level Expert Group on AI. However, the assessment process and the lessons learned can be adapted to other domains and include additional frameworks.

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：