Trustworthy Machine Learning

Author : Yada Pruksachatkun
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 109812023X
Total Pages : 304 pages
Book Rating : 4.38/5 ( download)

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Download or read book Practicing Trustworthy Machine Learning written by Yada Pruksachatkun and published by "O'Reilly Media, Inc.". This book was released on 2023-01-03 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 : Jianlong Zhou
Publisher : Springer
ISBN 13 : 3319904035
Total Pages : 482 pages
Book Rating : 4.30/5 ( download)

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Download or read book Human and Machine Learning written by Jianlong Zhou and published by Springer. This book was released on 2018-06-07 with total page 482 pages. Available in PDF, EPUB and Kindle. Book excerpt: With an evolutionary advancement of Machine Learning (ML) algorithms, a rapid increase of data volumes and a significant improvement of computation powers, machine learning becomes hot in different applications. However, because of the nature of “black-box” in ML methods, ML still needs to be interpreted to link human and machine learning for transparency and user acceptance of delivered solutions. This edited book addresses such links from the perspectives of visualisation, explanation, trustworthiness and transparency. The book establishes the link between human and machine learning by exploring transparency in machine learning, visual explanation of ML processes, algorithmic explanation of ML models, human cognitive responses in ML-based decision making, human evaluation of machine learning and domain knowledge in transparent ML applications. This is the first book of its kind to systematically understand the current active research activities and outcomes related to human and machine learning. The book will not only inspire researchers to passionately develop new algorithms incorporating human for human-centred ML algorithms, resulting in the overall advancement of ML, but also help ML practitioners proactively use ML outputs for informative and trustworthy decision making. This book is intended for researchers and practitioners involved with machine learning and its applications. The book will especially benefit researchers in areas like artificial intelligence, decision support systems and human-computer interaction.

Author : Bhavya Kailkhura
Publisher : Frontiers Media SA
ISBN 13 : 2889714144
Total Pages : 101 pages
Book Rating : 4.48/5 ( download)

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Download or read book Safe and Trustworthy Machine Learning written by Bhavya Kailkhura and published by Frontiers Media SA. This book was released on 2021-10-29 with total page 101 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Author : Kush R. Vashney
Publisher :
ISBN 13 :
Total Pages : 256 pages
Book Rating : 4.59/5 ( download)

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Download or read book Trustworthy Machine Learning written by Kush R. Vashney and published by . This book was released on 2022 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Author : Fredrik Heintz
Publisher :
ISBN 13 : 9783030739607
Total Pages : 0 pages
Book Rating : 4.00/5 ( download)

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Download or read book Trustworthy AI - Integrating Learning, Optimization and Reasoning written by Fredrik Heintz and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 : Beena Ammanath
Publisher : John Wiley & Sons
ISBN 13 : 1119867959
Total Pages : 230 pages
Book Rating : 4.51/5 ( download)

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Download or read book Trustworthy AI written by Beena Ammanath and published by John Wiley & Sons. This book was released on 2022-03-15 with total page 230 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 : Lance Eliot
Publisher :
ISBN 13 : 9780692800614
Total Pages : 210 pages
Book Rating : 4.11/5 ( download)

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Download or read book AI Guardian Angel Bots for Deep AI Trustworthiness written by Lance Eliot and published by . This book was released on 2016-10-24 with total page 210 pages. Available in PDF, EPUB and Kindle. Book excerpt: AI practitioner and noted "AI Insider" thought leader, Dr. Lance Eliot, MBA, PhD, provides ground breaking work on the emergence of AI Guardian Angel Bots. Dr. Eliot points out that with the advent of Deep AI and Machine Learning, there is both great promise and potential peril for consumers. Will people trust their self-driving cars and other smart devices as the Internet of Things (IoT) takes hold? Should they trust these Deep AI enabled systems? One means to bolster trust in Deep AI is to have consumers protect themselves and their safety by making use of AI Guardian Angel Bots. These new Bots are intended to monitor and potentially guide a Deep AI system that the consumer is dependent on. Your AI Guardian Angel Bot will be your guardian or protector when being driven by a self-driving car, and in any other situation that entails a dependency on Deep AI and Machine Learning. Readable by those interested in the latest in AI, this book is intended for business leaders, technology experts, and anyone with an interest in getting an edge on safety in the AI burgeoning world that we live in.

Author : Ben Shneiderman
Publisher : Oxford University Press
ISBN 13 : 0192845292
Total Pages : 390 pages
Book Rating : 4.90/5 ( download)

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Download or read book Human-Centered AI written by Ben Shneiderman and published by Oxford University Press. This book was released on 2022 with total page 390 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 : Wojciech Samek
Publisher : Springer Nature
ISBN 13 : 3030289540
Total Pages : 435 pages
Book Rating : 4.46/5 ( download)

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Download or read book Explainable AI: Interpreting, Explaining and Visualizing Deep Learning written by Wojciech Samek and published by Springer Nature. This book was released on 2019-09-10 with total page 435 pages. Available in PDF, EPUB and Kindle. Book excerpt: The development of “intelligent” systems that can take decisions and perform autonomously might lead to faster and more consistent decisions. A limiting factor for a broader adoption of AI technology is the inherent risks that come with giving up human control and oversight to “intelligent” machines. For sensitive tasks involving critical infrastructures and affecting human well-being or health, it is crucial to limit the possibility of improper, non-robust and unsafe decisions and actions. Before deploying an AI system, we see a strong need to validate its behavior, and thus establish guarantees that it will continue to perform as expected when deployed in a real-world environment. In pursuit of that objective, ways for humans to verify the agreement between the AI decision structure and their own ground-truth knowledge have been explored. Explainable AI (XAI) has developed as a subfield of AI, focused on exposing complex AI models to humans in a systematic and interpretable manner. The 22 chapters included in this book provide a timely snapshot of algorithms, theory, and applications of interpretable and explainable AI and AI techniques that have been proposed recently reflecting the current discourse in this field and providing directions of future development. The book is organized in six parts: towards AI transparency; methods for interpreting AI systems; explaining the decisions of AI systems; evaluating interpretability and explanations; applications of explainable AI; and software for explainable AI.

Author : Cynthia Dwork
Publisher :
ISBN 13 : 9781601988188
Total Pages : 286 pages
Book Rating : 4.84/5 ( download)

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Download or read book The Algorithmic Foundations of Differential Privacy written by Cynthia Dwork and published by . This book was released on 2014 with total page 286 pages. Available in PDF, EPUB and Kindle. Book excerpt: The problem of privacy-preserving data analysis has a long history spanning multiple disciplines. As electronic data about individuals becomes increasingly detailed, and as technology enables ever more powerful collection and curation of these data, the need increases for a robust, meaningful, and mathematically rigorous definition of privacy, together with a computationally rich class of algorithms that satisfy this definition. Differential Privacy is such a definition. The Algorithmic Foundations of Differential Privacy starts out by motivating and discussing the meaning of differential privacy, and proceeds to explore the fundamental techniques for achieving differential privacy, and the application of these techniques in creative combinations, using the query-release problem as an ongoing example. A key point is that, by rethinking the computational goal, one can often obtain far better results than would be achieved by methodically replacing each step of a non-private computation with a differentially private implementation. Despite some powerful computational results, there are still fundamental limitations. Virtually all the algorithms discussed herein maintain differential privacy against adversaries of arbitrary computational power -- certain algorithms are computationally intensive, others are efficient. Computational complexity for the adversary and the algorithm are both discussed. The monograph then turns from fundamentals to applications other than query-release, discussing differentially private methods for mechanism design and machine learning. The vast majority of the literature on differentially private algorithms considers a single, static, database that is subject to many analyses. Differential privacy in other models, including distributed databases and computations on data streams, is discussed. The Algorithmic Foundations of Differential Privacy is meant as a thorough introduction to the problems and techniques of differential privacy, and is an invaluable reference for anyone with an interest in the topic.