Connecting the dots in trustworthy Artificial Intelligence: From AI principles, ethics, and key requirements to responsible AI systems and regulation
出版年份 2023 全文链接
标题
Connecting the dots in trustworthy Artificial Intelligence: From AI principles, ethics, and key requirements to responsible AI systems and regulation
作者
关键词
-
出版物
Information Fusion
Volume -, Issue -, Pages 101896
出版商
Elsevier BV
发表日期
2023-06-25
DOI
10.1016/j.inffus.2023.101896
参考文献
相关参考文献
注意：仅列出部分参考文献，下载原文获取全部文献信息。- Ethics and governance of trustworthy medical artificial intelligence
- (2023) Jie Zhang et al. BMC Medical Informatics and Decision Making
- Trustworthy artificial intelligence and the European Union AI act: On the conflation of trustworthiness and acceptability of risk
- (2023) Johann Laux et al. Regulation & Governance
- Trustworthy AI—Part 1
- (2023) Riccardo Mariani et al. COMPUTER
- AI Maintenance: A Robustness Perspective
- (2023) Pin-Yu Chen et al. COMPUTER
- A Survey on Deep Learning Resilience Assessment Methodologies
- (2023) Annachiara Ruospo et al. COMPUTER
- Detecting Systematic Deviations in Data and Models
- (2023) Skyler Speakman et al. COMPUTER
- Three Levels of AI Transparency
- (2023) Kashyap Haresamudram et al. COMPUTER
- Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence
- (2023) Sajid Ali et al. Information Fusion
- Responsible and human centric AI-based insurance advisors
- (2023) Galena Pisoni et al. INFORMATION PROCESSING & MANAGEMENT
- Gender and sex bias in COVID-19 epidemiological data through the lens of causality
- (2023) Natalia Díaz-Rodríguez et al. INFORMATION PROCESSING & MANAGEMENT
- REVEL Framework to Measure Local Linear Explanations for Black-Box Models: Deep Learning Image Classification Case Study
- (2023) Iván Sevillano-García et al. INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
- Mind-reading machines are here: is it time to worry?
- (2023) Sara Reardon NATURE
- Rethink reporting of evaluation results in AI
- (2023) Ryan Burnell et al. SCIENCE
- GraphGONet: a self-explaining neural network encapsulating the Gene Ontology graph for phenotype prediction on gene expression
- (2022) Victoria Bourgeais et al. BIOINFORMATICS
- Taxonomy of Machine Learning Safety: A Survey and Primer
- (2022) Sina Mohseni et al. ACM COMPUTING SURVEYS
- The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink
- (2022) David Patterson et al. COMPUTER
- Open-world Machine Learning: Applications, Challenges, and Opportunities
- (2022) Jitendra Parmar et al. ACM COMPUTING SURVEYS
- Privacy, accuracy, and model fairness trade-offs in federated learning
- (2022) Xiuting Gu et al. COMPUTERS & SECURITY
- PLENARY: Explaining black-box models in natural language through fuzzy linguistic summaries
- (2022) Katarzyna Kaczmarek-Majer et al. INFORMATION SCIENCES
- Survey on Fully Homomorphic Encryption, Theory, and Applications
- (2022) Chiara Marcolla et al. PROCEEDINGS OF THE IEEE
- A survey on XAI and natural language explanations
- (2022) Erik Cambria et al. INFORMATION PROCESSING & MANAGEMENT
- Deep neural networks compression: A comparative survey and choice recommendations
- (2022) Giosué Cataldo Marinó et al. NEUROCOMPUTING
- Communicating Missing Causal Information to Explain a Robot’s Past Behavior
- (2022) Zhao Han et al. ACM Transactions on Human-Robot Interaction
- Human-Centered Artificial Intelligence for Designing Accessible Cultural Heritage
- (2021) Galena Pisoni et al. Applied Sciences-Basel
- Ethical Machines: The Human-centric Use of Artificial Intelligence
- (2021) B. Lepri et al. iScience
- Missing the missing values: The ugly duckling of fairness in machine learning
- (2021) Martínez‐Plumed Fernando et al. INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
- Managing bias and unfairness in data for decision support: a survey of machine learning and data engineering approaches to identify and mitigate bias and unfairness within data management and analytics systems
- (2021) Agathe Balayn et al. VLDB JOURNAL
- A Survey on Bias and Fairness in Machine Learning
- (2021) Ninareh Mehrabi et al. ACM COMPUTING SURVEYS
- The Ten Commandments of Ethical Medical AI
- (2021) Heimo Muller et al. COMPUTER
- Medical artificial intelligence
- (2021) Karl Stöger et al. COMMUNICATIONS OF THE ACM
- Notions of explainability and evaluation approaches for explainable artificial intelligence
- (2021) Giulia Vilone et al. Information Fusion
- A review of uncertainty quantification in deep learning: Techniques, applications and challenges
- (2021) Moloud Abdar et al. Information Fusion
- Conformity Assessments and Post-market Monitoring: A Guide to the Role of Auditing in the Proposed European AI Regulation
- (2021) Jakob Mökander et al. MINDS AND MACHINES
- EXplainable Neural-Symbolic Learning (X-NeSyL) methodology to fuse deep learning representations with expert knowledge graphs: The MonuMAI cultural heritage use case
- (2021) Natalia Díaz-Rodríguez et al. Information Fusion
- Information fusion as an integrative cross-cutting enabler to achieve robust, explainable, and trustworthy medical artificial intelligence
- (2021) Andreas Holzinger et al. Information Fusion
- AI for social good: unlocking the opportunity for positive impact
- (2020) Nenad Tomašev et al. Nature Communications
- Federated Learning and Differential Privacy: Software tools analysis, the Sherpa.ai FL framework and methodological guidelines for preserving data privacy
- (2020) Nuria Rodríguez-Barroso et al. Information Fusion
- MINIMAR (MINimum Information for Medical AI Reporting): Developing reporting standards for artificial intelligence in health care
- (2020) Tina Hernandez-Boussard et al. JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
- Green AI
- (2020) Roy Schwartz et al. COMMUNICATIONS OF THE ACM
- A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI
- (2020) Erico Tjoa et al. IEEE Transactions on Neural Networks and Learning Systems
- Machine Learning Interpretability: A Survey on Methods and Metrics
- (2019) Diogo V. Carvalho et al. Electronics
- Artificial Intelligence, Responsibility Attribution, and a Relational Justification of Explainability
- (2019) Mark Coeckelbergh SCIENCE AND ENGINEERING ETHICS
- Factual and Counterfactual Explanations for Black Box Decision Making
- (2019) Riccardo Guidotti et al. IEEE INTELLIGENT SYSTEMS
- Addressing Bias in Artificial Intelligence in Health Care
- (2019) Ravi B. Parikh et al. JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION
- Continual learning for robotics: Definition, framework, learning strategies, opportunities and challenges
- (2019) Timothée Lesort et al. Information Fusion
- Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
- (2019) Alejandro Barredo Arrieta et al. Information Fusion
- Bias on the web
- (2018) Ricardo Baeza-Yates COMMUNICATIONS OF THE ACM
- Ethics and Privacy in AI and Big Data: Implementing Responsible Research and Innovation
- (2018) Bernd Carsten Stahl et al. IEEE SECURITY & PRIVACY
- Quality and Diversity Optimization: A Unifying Modular Framework
- (2018) Antoine Cully et al. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
- A systematic review of provenance systems
- (2018) Beatriz Pérez et al. KNOWLEDGE AND INFORMATION SYSTEMS
Find Funding. Review Successful Grants.
Explore over 25,000 new funding opportunities and over 6,000,000 successful grants.
ExploreAdd your recorded webinar
Do you already have a recorded webinar? Grow your audience and get more views by easily listing your recording on Peeref.
Upload Now