Safe and Trustworthy Machine Learning PDF Download

Author: Bhavya Kailkhura
Publisher: Frontiers Media SA
ISBN: 2889714144
Category : Science
Languages : en
Pages : 101

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Author: Yada Pruksachatkun
Publisher: "O'Reilly Media, Inc."
ISBN: 109812023X
Category : Computers
Languages : en
Pages : 304

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Book Description
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

Author: Hao Chen
Publisher: Springer Nature
ISBN: 3031395395
Category : Computers
Languages : en
Pages : 207

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Book Description
This book constitutes the proceedings of First International Workshop, TML4H 2023, held virtually, in May 2023. The 16 full papers included in this volume were carefully reviewed and selected from 30 submissions. The goal of this workshop is to bring together experts from academia, clinic, and industry with an insightful vision of promoting trustworthy machine learning in healthcare in terms of scalability, accountability, and explainability.

Author: Ben Shneiderman
Publisher: Oxford University Press
ISBN: 0192845292
Category : Computers
Languages : en
Pages : 390

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Book Description
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.

Author: Beena Ammanath
Publisher: John Wiley & Sons
ISBN: 1119867959
Category : Computers
Languages : en
Pages : 230

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Book Description
An essential resource on artificial intelligence ethics for business leaders In Trustworthy AI, award-winning executive Beena Ammanath offers a practical approach for enterprise leaders to manage business risk in a world where AI is everywhere by understanding the qualities of trustworthy AI and the essential considerations for its ethical use within the organization and in the marketplace. The author draws from her extensive experience across different industries and sectors in data, analytics and AI, the latest research and case studies, and the pressing questions and concerns business leaders have about the ethics of AI. Filled with deep insights and actionable steps for enabling trust across the entire AI lifecycle, the book presents: In-depth investigations of the key characteristics of trustworthy AI, including transparency, fairness, reliability, privacy, safety, robustness, and more A close look at the potential pitfalls, challenges, and stakeholder concerns that impact trust in AI application Best practices, mechanisms, and governance considerations for embedding AI ethics in business processes and decision making Written to inform executives, managers, and other business leaders, Trustworthy AI breaks new ground as an essential resource for all organizations using AI.

Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 0

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Author: Kush R. Vashney
Publisher:
ISBN:
Category : Machine learning
Languages : en
Pages : 256

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Book Description

Author: Fredrik Heintz
Publisher: Springer Nature
ISBN: 3030739597
Category : Computers
Languages : en
Pages : 278

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Book Description
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.

Author: Yada Pruksachatkun
Publisher: "O'Reilly Media, Inc."
ISBN: 1098120248
Category : Computers
Languages : en
Pages : 303

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Book Description
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

Author: National Research Council
Publisher: National Academies Press
ISBN: 0309131820
Category : Computers
Languages : en
Pages : 352

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Book Description
Whether or not you use a computer, you probably use a telephone, electric power, and a bank. Although you may not be aware of their presence, networked computer systems are increasingly becoming an integral part of your daily life. Yet, if such systems perform poorly or don't work at all, then they can put life, liberty, and property at tremendous risk. Is the trust that weâ€"as individuals and as a societyâ€"are placing in networked computer systems justified? And if it isn't, what can we do to make such systems more trustworthy? This book provides an assessment of the current state of the art procedures for building trustworthy networked information systems. It proposes directions for research in computer and network security, software technology, and system architecture. In addition, the book assesses current technical and market trends in order to better inform public policy as to where progress is likely and where incentives could help. Trust in Cyberspace offers insights into: The strengths and vulnerabilities of the telephone network and Internet, the two likely building blocks of any networked information system. The interplay between various dimensions of trustworthiness: environmental disruption, operator error, "buggy" software, and hostile attack. The implications for trustworthiness of anticipated developments in hardware and software technology, including the consequences of mobile code. The shifts in security technology and research resulting from replacing centralized mainframes with networks of computers. The heightened concern for integrity and availability where once only secrecy mattered. The way in which federal research funding levels and practices have affected the evolution and current state of the science and technology base in this area. You will want to read this book if your life is touched in any way by computers or telecommunications. But then, whose life isn't?