Author: Adnan Masood
Publisher: Packt Publishing Ltd
ISBN: 1803249668
Category : Computers
Languages : en
Pages : 318
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Book Description
Build and deploy your AI models successfully by exploring model governance, fairness, bias, and potential pitfalls Purchase of the print or Kindle book includes a free PDF eBook Key Features Learn ethical AI principles, frameworks, and governance Understand the concepts of fairness assessment and bias mitigation Introduce explainable AI and transparency in your machine learning models Book DescriptionResponsible AI in the Enterprise is a comprehensive guide to implementing ethical, transparent, and compliant AI systems in an organization. With a focus on understanding key concepts of machine learning models, this book equips you with techniques and algorithms to tackle complex issues such as bias, fairness, and model governance. Throughout the book, you’ll gain an understanding of FairLearn and InterpretML, along with Google What-If Tool, ML Fairness Gym, IBM AI 360 Fairness tool, and Aequitas. You’ll uncover various aspects of responsible AI, including model interpretability, monitoring and management of model drift, and compliance recommendations. You’ll gain practical insights into using AI governance tools to ensure fairness, bias mitigation, explainability, privacy compliance, and privacy in an enterprise setting. Additionally, you’ll explore interpretability toolkits and fairness measures offered by major cloud AI providers like IBM, Amazon, Google, and Microsoft, while discovering how to use FairLearn for fairness assessment and bias mitigation. You’ll also learn to build explainable models using global and local feature summary, local surrogate model, Shapley values, anchors, and counterfactual explanations. By the end of this book, you’ll be well-equipped with tools and techniques to create transparent and accountable machine learning models.What you will learn Understand explainable AI fundamentals, underlying methods, and techniques Explore model governance, including building explainable, auditable, and interpretable machine learning models Use partial dependence plot, global feature summary, individual condition expectation, and feature interaction Build explainable models with global and local feature summary, and influence functions in practice Design and build explainable machine learning pipelines with transparency Discover Microsoft FairLearn and marketplace for different open-source explainable AI tools and cloud platforms Who this book is for This book is for data scientists, machine learning engineers, AI practitioners, IT professionals, business stakeholders, and AI ethicists who are responsible for implementing AI models in their organizations.
Author: Mark Treveil
Publisher: "O'Reilly Media, Inc."
ISBN: 1098116429
Category : Computers
Languages : en
Pages : 171
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Book Description
More than half of the analytics and machine learning (ML) models created by organizations today never make it into production. Some of the challenges and barriers to operationalization are technical, but others are organizational. Either way, the bottom line is that models not in production can't provide business impact. This book introduces the key concepts of MLOps to help data scientists and application engineers not only operationalize ML models to drive real business change but also maintain and improve those models over time. Through lessons based on numerous MLOps applications around the world, nine experts in machine learning provide insights into the five steps of the model life cycle--Build, Preproduction, Deployment, Monitoring, and Governance--uncovering how robust MLOps processes can be infused throughout. This book helps you: Fulfill data science value by reducing friction throughout ML pipelines and workflows Refine ML models through retraining, periodic tuning, and complete remodeling to ensure long-term accuracy Design the MLOps life cycle to minimize organizational risks with models that are unbiased, fair, and explainable Operationalize ML models for pipeline deployment and for external business systems that are more complex and less standardized
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: OECD
Publisher: OECD Publishing
ISBN: 9264764836
Category :
Languages : en
Pages : 164
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Book Description
The OECD Business and Finance Outlook is an annual publication that presents unique data and analysis on the trends, both positive and negative, that are shaping tomorrow’s world of business, finance and investment.
Author: Jun Chen
Publisher: CRC Press
ISBN: 1000220168
Category : Business & Economics
Languages : en
Pages : 165
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Book Description
Based on interdisciplinary research into "Directional Change", a new data-driven approach to financial data analysis, Detecting Regime Change in Computational Finance: Data Science, Machine Learning and Algorithmic Trading applies machine learning to financial market monitoring and algorithmic trading. Directional Change is a new way of summarising price changes in the market. Instead of sampling prices at fixed intervals (such as daily closing in time series), it samples prices when the market changes direction ("zigzags"). By sampling data in a different way, this book lays out concepts which enable the extraction of information that other market participants may not be able to see. The book includes a Foreword by Richard Olsen and explores the following topics: Data science: as an alternative to time series, price movements in a market can be summarised as directional changes Machine learning for regime change detection: historical regime changes in a market can be discovered by a Hidden Markov Model Regime characterisation: normal and abnormal regimes in historical data can be characterised using indicators defined under Directional Change Market Monitoring: by using historical characteristics of normal and abnormal regimes, one can monitor the market to detect whether the market regime has changed Algorithmic trading: regime tracking information can help us to design trading algorithms It will be of great interest to researchers in computational finance, machine learning and data science. About the Authors Jun Chen received his PhD in computational finance from the Centre for Computational Finance and Economic Agents, University of Essex in 2019. Edward P K Tsang is an Emeritus Professor at the University of Essex, where he co-founded the Centre for Computational Finance and Economic Agents in 2002.
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Author: Bistra Vassileva
Publisher:
ISBN: 9781799864387
Category : Technology & Engineering
Languages : en
Pages : 284
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Book Description
"This book is aimed at scholars and practitioners who want to widen their understanding of artificial intelligence out of the 'narrow' technical perspective to a more broad viewpoint that embraces the links between AI theory, practice, and policy"--
Author: Prashant Natarajan
Publisher: CRC Press
ISBN: 1351032925
Category : Computers
Languages : en
Pages : 465
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Book Description
Artificial intelligence (AI) in its various forms –– machine learning, chatbots, robots, agents, etc. –– is increasingly being seen as a core component of enterprise business workflow and information management systems. The current promise and hype around AI are being driven by software vendors, academic research projects, and startups. However, we posit that the greatest promise and potential for AI lies in the enterprise with its applications touching all organizational facets. With increasing business process and workflow maturity, coupled with recent trends in cloud computing, datafication, IoT, cybersecurity, and advanced analytics, there is an understanding that the challenges of tomorrow cannot be solely addressed by today’s people, processes, and products. There is still considerable mystery, hype, and fear about AI in today’s world. A considerable amount of current discourse focuses on a dystopian future that could adversely affect humanity. Such opinions, with understandable fear of the unknown, don’t consider the history of human innovation, the current state of business and technology, or the primarily augmentative nature of tomorrow’s AI. This book demystifies AI for the enterprise. It takes readers from the basics (definitions, state-of-the-art, etc.) to a multi-industry journey, and concludes with expert advice on everything an organization must do to succeed. Along the way, we debunk myths, provide practical pointers, and include best practices with applicable vignettes. AI brings to enterprise the capabilities that promise new ways by which professionals can address both mundane and interesting challenges more efficiently, effectively, and collaboratively (with humans). The opportunity for tomorrow’s enterprise is to augment existing teams and resources with the power of AI in order to gain competitive advantage, discover new business models, establish or optimize new revenues, and achieve better customer and user satisfaction.
Author: Olivia Gambelin
Publisher: Kogan Page Publishers
ISBN: 1398616028
Category : Business & Economics
Languages : en
Pages : 273
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Book Description
Responsible AI is a guide to how business leaders can develop and implement a robust and responsible AI strategy for their organizations. Responsible AI has rapidly transitioned to a strategic priority for leaders and organizations worldwide. Responsible AI guides readers step-by-step through the process of establishing robust yet manageable ethical AI initiatives for any size organization, outlining the three core pillars of building a responsible AI strategy: people, process and technology. It provides the insight and guidance needed to help leaders fully understand the technical and commercial potential of ethics in AI while also covering the operations and strategy needed to support implementation. Responsible AI breaks down what it means to use ethics and values as a modern-day decision-making tool in the design and development of AI. It conceptually covers both how ethics can be used to identify risks and establish safeguards in the development of AI and how to use ethics-by-design methods to stimulate AI innovation. It also covers the different considerations for large enterprises and SMEs and discusses the role of the AI ethicist. It is supported by practical case studies from organizations such as IKEA, Nvidia, Rolls-Royce and NatWest Group.
Author: Vassileva, Bistra
Publisher: IGI Global
ISBN: 179984286X
Category : Technology & Engineering
Languages : en
Pages : 259
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Book Description
The research surrounding artificial intelligence (AI) is vast and quite diverse in both its applied and theoretical fields. AI tools and techniques, such as machine learning, data mining, neural networks, and advanced analytics, are evolving at a high speed, creating a consistent need for updated research. This is especially relevant with frequent developments for the application of AI technology in many science and industry sectors. This rapid expansion created a need for research that focuses on the questions surrounding the development of AI such as ethical issues, responsible AI methods and applications, and its widespread implementation. Within the answers to these questions is the prevailing notion that AI should be accountable, explainable, transparent, and fair for all organizations and individuals. Responsible AI and Ethical Issues for Businesses and Governments widens the understanding of AI outside of the “narrow” technical perspective to a broader viewpoint that embraces the links between AI theory, practice, and policy. The chapters in this book discuss the basic philosophical and conceptual foundations of AI and explores the responsible application of AI tools and methods, the moral aspects of AI, practical issues, and responsible AI implementation across a range of industries. While highlighting topics that include digital transformation, ethical competence, information literacy in AI, and the interaction between AI and humans, this book is ideally designed for IT specialists, technology developers, technologists, ethicists, practitioners, stakeholders, academicians, students, and researchers who are interested in learning more about the ethical and responsible use of AI.
