Safe And Trustworthy Machine Learning
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Safe And Trustworthy Machine Learning
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Author : Bhavya Kailkhura
language : en
Publisher: Frontiers Media SA
Release Date : 2021-10-29
Safe And Trustworthy Machine Learning written by Bhavya Kailkhura and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-29 with Science categories.
Practicing Trustworthy Machine Learning
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Author : Yada Pruksachatkun
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2023-01-03
Practicing Trustworthy Machine Learning written by Yada Pruksachatkun and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-01-03 with Business & Economics categories.
With the increasing use of AI in high-stakes domains such as medicine, law, and defense, organizations spend a lot of time and money to make ML models trustworthy. Many books on the subject offer deep dives into theories and concepts. This guide provides a practical starting point to help development teams produce models that are secure, more robust, less biased, and more explainable. Authors Yada Pruksachatkun, Matthew McAteer, and Subhabrata Majumdar translate best practices in the academic literature for curating datasets and building models into a blueprint for building industry-grade trusted ML systems. With this book, engineers and data scientists will gain a much-needed foundation for releasing trustworthy ML applications into a noisy, messy, and often hostile world. You'll learn: Methods to explain ML models and their outputs to stakeholders How to recognize and fix fairness concerns and privacy leaks in an ML pipeline How to develop ML systems that are robust and secure against malicious attacks Important systemic considerations, like how to manage trust debt and which ML obstacles require human intervention
Machine Learning And Knowledge Discovery In Databases
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Author : Massih-Reza Amini
language : en
Publisher: Springer Nature
Release Date : 2023-03-16
Machine Learning And Knowledge Discovery In Databases written by Massih-Reza Amini and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-03-16 with Computers categories.
The multi-volume set LNAI 13713 until 13718 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2022, which took place in Grenoble, France, in September 2022. The 236 full papers presented in these proceedings were carefully reviewed and selected from a total of 1060 submissions. In addition, the proceedings include 17 Demo Track contributions. The volumes are organized in topical sections as follows: Part I: Clustering and dimensionality reduction; anomaly detection; interpretability and explainability; ranking and recommender systems; transfer and multitask learning; Part II: Networks and graphs; knowledge graphs; social network analysis; graph neural networks; natural language processing and text mining; conversational systems; Part III: Deep learning; robust and adversarial machine learning; generative models; computer vision; meta-learning, neural architecture search; Part IV: Reinforcement learning; multi-agent reinforcement learning; bandits and online learning; active and semi-supervised learning; private and federated learning; . Part V: Supervised learning; probabilistic inference; optimal transport; optimization; quantum, hardware; sustainability; Part VI: Time series; financial machine learning; applications; applications: transportation; demo track.
Artificial Intelligence Data And Model Safety
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Author : Yu-Gang Jiang
language : en
Publisher: Elsevier
Release Date : 2025-09-01
Artificial Intelligence Data And Model Safety written by Yu-Gang Jiang and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-09-01 with Computers categories.
Artificial Intelligence Data and Model Safety: Risks, Attacks and Defenses begins with a brief review of the history of AI and AI security and then introduces the fundamental aspects of machine learning and AI security. Two key aspects are covered: data safety and modeling. It provides detailed explanations of a wide range of attacks and defense algorithms related to data security, as well as adversarial attack/defense, backdoor attack/defense, and extraction attack/defense algorithms related to model security. By providing a systematic, comprehensive, and in-depth introduction to the topic, this book help readers understand the advanced attack and defense techniques in the field of AI security. - Systematic: comprehensively introduces AI safety, covering both attack and defense technologies - In-depth: covers a broad range of attack and defense strategies from the perspectives of adversarial learning and robust optimization, providing detailed explanations and insights - Includes the latest research developments and state-of-the-art techniques in the field of AI safety
Human Centered Ai
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Author : Ben Shneiderman
language : en
Publisher: Oxford University Press
Release Date : 2022
Human Centered Ai written by Ben Shneiderman and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with Computers categories.
The remarkable progress in algorithms for machine and deep learning have opened the doors to new opportunities, and some dark possibilities. However, a bright future awaits those who build on their working methods by including HCAI strategies of design and testing. As many technology companies and thought leaders have argued, the goal is not to replace people, but to empower them by making design choices that give humans control over technology. In Human-Centered AI, Professor Ben Shneiderman offers an optimistic realist's guide to how artificial intelligence can be used to augment and enhance humans' lives. This project bridges the gap between ethical considerations and practical realities to offer a road map for successful, reliable systems. Digital cameras, communications services, and navigation apps are just the beginning. Shneiderman shows how future applications will support health and wellness, improve education, accelerate business, and connect people in reliable, safe, and trustworthy ways that respect human values, rights, justice, and dignity.
Trustworthy Ai Integrating Learning Optimization And Reasoning
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Author : Fredrik Heintz
language : en
Publisher: Springer Nature
Release Date : 2021-04-12
Trustworthy Ai Integrating Learning Optimization And Reasoning written by Fredrik Heintz and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-12 with Computers categories.
This book constitutes the thoroughly refereed conference proceedings of the First International Workshop on the Foundation of Trustworthy AI - Integrating Learning, Optimization and Reasoning, TAILOR 2020, held virtually in September 2020, associated with ECAI 2020, the 24th European Conference on Artificial Intelligence. The 11 revised full papers presented together with 6 short papers and 6 position papers were reviewed and selected from 52 submissions. The contributions address various issues for Trustworthiness, Learning, reasoning, and optimization, Deciding and Learning How to Act, AutoAI, and Reasoning and Learning in Social Contexts.
Open Ai And Computational Intelligence For Society 5 0
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Author : Pandey, Rajiv
language : en
Publisher: IGI Global
Release Date : 2024-11-29
Open Ai And Computational Intelligence For Society 5 0 written by Pandey, Rajiv and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-29 with Computers categories.
As technology rapidly advances, the complexity of societal challenges grows, necessitating intelligent solutions that can adapt and evolve. However, developing such solutions requires a deep understanding of computational intelligence (CI) and its application in addressing real-world problems. Moreover, ethical considerations surrounding AI, such as bias and accountability, are crucial to ensure responsible development and deployment of intelligent systems. Open AI and Computational Intelligence for Society 5.0 offers a comprehensive exploration of CI, providing insights into intelligent systems' theory, design, and application. This book is a practical guide for scientists, engineers, and researchers seeking to develop thoughtful solutions for complex societal issues. Integrating disruptive technologies and frameworks illuminates the path toward creating intelligent machines collaborating with humans to enhance problem-solving and improve quality of life.
Navigating The Evolving Landscape Of Safety Standards For Machine Learning Based Road Vehicle Functions
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Author : Simon Burton
language : en
Publisher: SAE International
Release Date : 2024-08-26
Navigating The Evolving Landscape Of Safety Standards For Machine Learning Based Road Vehicle Functions written by Simon Burton and has been published by SAE International this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-26 with Technology & Engineering categories.
ML approaches to solving some of the key perception and decision challenges in automated vehicle functions are maturing at an incredible rate. However, the setbacks experienced during initial attempts at widespread deployment have highlighted the need for a careful consideration of safety during the development and deployment of these functions. To better control the risk associated with this storm of complex functionality, open operating environments, and cutting-edge technology, there is a need for industry consensus on best practices for achieving an acceptable level of safety. Navigating the Evolving Landscape of Safety Standards for Machine Learning-based Road Vehicle Functions provides an overview of standards relevant to the safety of ML-based vehicle functions and serves as guidance for technology providers—including those new to the automotive sector—on how to interpret the evolving standardization landscape. The report also contains practical guidance, along with an example from the perspective of a developer of an ML-based perception function on how to interpret the requirements of these standards. Click here to access the full SAE EDGETM Research Report portfolio. https://doi.org/10.4271/EPR2024017
Uncertainty For Safe Utilization Of Machine Learning In Medical Imaging
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Author : Carole H. Sudre
language : en
Publisher: Springer Nature
Release Date : 2024-10-02
Uncertainty For Safe Utilization Of Machine Learning In Medical Imaging written by Carole H. Sudre and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-02 with Computers categories.
This book constitutes the refereed proceedings of the 6th Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, on October 10, 2024. The 20 full papers presented in this book were carefully reviewed and selected from 28 submissions. They are organized in the following topical sections: annotation uncertainty; clinical implementation of uncertainty modelling and risk management in clinical pipelines; out of distribution and domain shift identification and management; uncertainty modelling and estimation.
Explainable Artificial Intelligence
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Author : Luca Longo
language : en
Publisher: Springer Nature
Release Date : 2024-07-09
Explainable Artificial Intelligence written by Luca Longo and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-09 with Computers categories.
This four-volume set constitutes the refereed proceedings of the Second World Conference on Explainable Artificial Intelligence, xAI 2024, held in Valletta, Malta, during July 17-19, 2024. The 95 full papers presented were carefully reviewed and selected from 204 submissions. The conference papers are organized in topical sections on: Part I - intrinsically interpretable XAI and concept-based global explainability; generative explainable AI and verifiability; notion, metrics, evaluation and benchmarking for XAI. Part II - XAI for graphs and computer vision; logic, reasoning, and rule-based explainable AI; model-agnostic and statistical methods for eXplainable AI. Part III - counterfactual explanations and causality for eXplainable AI; fairness, trust, privacy, security, accountability and actionability in eXplainable AI. Part IV - explainable AI in healthcare and computational neuroscience; explainable AI for improved human-computer interaction and software engineering for explainability; applications of explainable artificial intelligence.