Principles Of Ai Governance And Model Risk Management
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Principles Of Ai Governance And Model Risk Management
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Author : James Sayles
language : en
Publisher: Springer Nature
Release Date : 2024-12-27
Principles Of Ai Governance And Model Risk Management written by James Sayles and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-27 with Computers categories.
Navigate the complex landscape of Artificial Intelligence (AI) governance and model risk management using a holistic approach encompassing people, processes, and technology. This book provides practical guidance, oversight structure and centers of excellence, and actionable insights for organizations seeking to harness the power of AI responsibly, ethically, and transparently. By addressing the technical, ethical, and societal dimensions of AI governance, organizations will be empowered to build trustworthy AI systems that benefit both their bottom line and the broader community. Featuring successful mitigating controls based on proven use cases, the book underscores the importance of aligning AI strategy with AI governance, striking a balance between AI innovation, risk mitigation as well as broader business goals. You’ll receive pointers for designing a well-governed AI development lifecycle, emphasizing transparency, accountability, and continuous monitoring throughout the AI development lifecycle. This book highlights the importance of collaboration between stakeholders, i.e., boards of directors, CxOs, corporate counsel, compliance officers, audit executives, data scientists, developers, validators, etc. You’ll gain practical advice on addressing the challenges related to the ownership of AI-generated content and models, stressing the need for legal frameworks and international collaboration. You’ll also learn the importance of auditing AI systems, developing protocols for rapid response in case of AI-related crises, and building capacity for AI actors through education. Principles of AI Governance and Model Risk Management demonstrates its value-added uniqueness by detailing a strategy to ensure a cohesive approach to managing AI-related risks, global compliance, policy, privacy, and AI-human collaboration and oversight. What You Will Learn Different approaches to AI adoption, from building in-house AI capabilities to partnering with external providers Key factors to consider when choosing an AI solution and how to ensure its successful integration into existing workflows AI technologies, their business impact, and ethical considerations to make informed decisions and foster responsible AI The environmental impacts of AI systems and the need for sustainable practices in AI development and deployment. Who This Book is For Business executives and process owners/representatives, risk officers, cybersecurity professionals, legal counsel and ethics officers, human resource professionals, data scientists, AI developers, and CTOs.
Principles Of Ai Governance And Model Risk Management
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Author : James Sayles
language : en
Publisher: Apress
Release Date : 2024-12-03
Principles Of Ai Governance And Model Risk Management written by James Sayles and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-03 with Business & Economics categories.
Navigate the complex landscape of Artificial Intelligence (AI) governance and model risk management using a holistic approach encompassing people, processes, and technology. This book provides practical guidance, oversight structure and centers of excellence, and actionable insights for organizations seeking to harness the power of AI responsibly, ethically, and transparently. By addressing the technical, ethical, and societal dimensions of AI governance, organizations will be empowered to build trustworthy AI systems that benefit both their bottom line and the broader community. Featuring successful mitigating controls based on proven use cases, the book underscores the importance of aligning AI strategy with AI governance, striking a balance between AI innovation, risk mitigation as well as broader business goals. You'll receive pointers for designing a well-governed AI development lifecycle, emphasizing transparency, accountability, and continuous monitoring throughout the AI development lifecycle. This book highlights the importance of collaboration between stakeholders, i.e., boards of directors, CxOs, corporate counsel, compliance officers, audit executives, data scientists, developers, validators, etc. You'll gain practical advice on addressing the challenges related to the ownership of AI-generated content and models, stressing the need for legal frameworks and international collaboration. You'll also learn the importance of auditing AI systems, developing protocols for rapid response in case of AI-related crises, and building capacity for AI actors through education. Principles of AI Governance and Model Risk Management demonstrates its value-added uniqueness by detailing a strategy to ensure a cohesive approach to managing AI-related risks, global compliance, policy, privacy, and AI-human collaboration and oversight. What You Will Learn Different approaches to AI adoption, from building in-house AI capabilities to partnering with external providers Key factors to consider when choosing an AI solution and how to ensure its successful integration into existing workflows AI technologies, their business impact, and ethical considerations to make informed decisions and foster responsible AI The environmental impacts of AI systems and the need for sustainable practices in AI development and deployment. Who This Book is For Business executives and process owners/representatives, risk officers, cybersecurity professionals, legal counsel and ethics officers, human resource professionals, data scientists, AI developers, and CTOs.
Interpretable Machine Learning
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Author : Christoph Molnar
language : en
Publisher: Lulu.com
Release Date : 2020
Interpretable Machine Learning written by Christoph Molnar and has been published by Lulu.com this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Computers categories.
This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
Next Generation Risk And Compliance Frameworks Ai Governance And Intelligent Automation In Global Banking Systems
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Author : Srinivasarao Paleti
language : en
Publisher: Global Pen Press UK
Release Date :
Next Generation Risk And Compliance Frameworks Ai Governance And Intelligent Automation In Global Banking Systems written by Srinivasarao Paleti and has been published by Global Pen Press UK this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
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Disrupting Finance
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Author : Theo Lynn
language : en
Publisher: Palgrave Pivot
Release Date : 2018-12-19
Disrupting Finance written by Theo Lynn and has been published by Palgrave Pivot this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-12-19 with Business & Economics categories.
This open access Pivot demonstrates how a variety of technologies act as innovation catalysts within the banking and financial services sector. Traditional banks and financial services are under increasing competition from global IT companies such as Google, Apple, Amazon and PayPal whilst facing pressure from investors to reduce costs, increase agility and improve customer retention. Technologies such as blockchain, cloud computing, mobile technologies, big data analytics and social media therefore have perhaps more potential in this industry and area of business than any other. This book defines a fintech ecosystem for the 21st century, providing a state-of-the art review of current literature, suggesting avenues for new research and offering perspectives from business, technology and industry.
Responsible Ai In The Enterprise
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Author : Adnan Masood
language : en
Publisher: Packt Publishing Ltd
Release Date : 2023-07-31
Responsible Ai In The Enterprise written by Adnan Masood and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-31 with Computers categories.
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.
Data Governance Devsecops And Advancements In Modern Software
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Author : Elbaghazaoui, Bahaa Eddine
language : en
Publisher: IGI Global
Release Date : 2025-04-24
Data Governance Devsecops And Advancements In Modern Software written by Elbaghazaoui, Bahaa Eddine and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-24 with Computers categories.
In today’s digital landscape, data governance, DevSecOps, and advancements in modern software development have become critical in secure and efficient technology ecosystems. As organizations rely on large amounts of data and sophisticated software systems to drive innovation and business success, the need for improved frameworks to manage, protect, and optimize this data increases. Data governance ensures data is accurate, secure, and compliant with regulations, while DevSecOps, an integrated approach to development, security, and operations, empowers teams to build, test, and utilize software with security embedded through its lifecycle. Along with the latest advancements in modern software technologies, these concepts form the foundation for building resilient, secure, and scalable applications. The intersection of these practices shapes the future of how software is developed, deployed, and governed, and further research may provide both opportunities and challenges for connection. Data Governance, DevSecOps, and Advancements in Modern Software explores the integration of key technologies and methodologies that define the modern digital landscape, with a focus on DataOps, DevSecOps, data governance, and software architecture. It provides a comprehensive guide to managing data workflows and enhancing operational efficiency while embedding security at every stage of the development lifecycle. This book covers topics such as data science, artificial intelligence, and resilient systems, and is a useful resource for data scientists, engineers, software developers, business owners, researchers, and academicians.
The Ai Engineer S Guide To Surviving The Eu Ai Act
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Author : Larysa Visengeriyeva
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2025-08-05
The Ai Engineer S Guide To Surviving The Eu Ai Act written by Larysa Visengeriyeva 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 2025-08-05 with Business & Economics categories.
With the introduction of the EU AI Act, companies employing AI systems face a new set of comprehensive and stringent regulations. Dr. Larysa Visengeriyeva offers a much-needed guide for navigating these unfamiliar regulatory waters to help you meet compliance challenges with confidence. From explaining the legislative framework to sharing strategies for implementing robust MLOps and data governance practices, this wide-ranging book shows you the way to thrive, not just survive, under the EU AI Act. It's an indispensable tool for engineers, data scientists, and policymakers engaged in or planning for AI deployments within the EU. By reading, you'll gain: An in-depth understanding of the EU AI Act, including the four risk categories and what they mean for you Strategies for compliance, including practical approaches to achieving technical readiness Actionable advice on applying MLOps methodologies to ensure ongoing compliance Insights on the implications of the EU's pioneering approach to AI regulation and its global effects
Risk Modeling
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Author : Terisa Roberts
language : en
Publisher: John Wiley & Sons
Release Date : 2022-09-20
Risk Modeling written by Terisa Roberts and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-09-20 with Business & Economics categories.
A wide-ranging overview of the use of machine learning and AI techniques in financial risk management, including practical advice for implementation Risk Modeling: Practical Applications of Artificial Intelligence, Machine Learning, and Deep Learning introduces readers to the use of innovative AI technologies for forecasting and evaluating financial risks. Providing up-to-date coverage of the practical application of current modelling techniques in risk management, this real-world guide also explores new opportunities and challenges associated with implementing machine learning and artificial intelligence (AI) into the risk management process. Authors Terisa Roberts and Stephen Tonna provide readers with a clear understanding about the strengths and weaknesses of machine learning and AI while explaining how they can be applied to both everyday risk management problems and to evaluate the financial impact of extreme events such as global pandemics and changes in climate. Throughout the text, the authors clarify misconceptions about the use of machine learning and AI techniques using clear explanations while offering step-by-step advice for implementing the technologies into an organization's risk management model governance framework. This authoritative volume: Highlights the use of machine learning and AI in identifying procedures for avoiding or minimizing financial risk Discusses practical tools for assessing bias and interpretability of resultant models developed with machine learning algorithms and techniques Covers the basic principles and nuances of feature engineering and common machine learning algorithms Illustrates how risk modeling is incorporating machine learning and AI techniques to rapidly consume complex data and address current gaps in the end-to-end modelling lifecycle Explains how proprietary software and open-source languages can be combined to deliver the best of both worlds: for risk models and risk practitioners Risk Modeling: Practical Applications of Artificial Intelligence, Machine Learning, and Deep Learning is an invaluable guide for CEOs, CROs, CFOs, risk managers, business managers, and other professionals working in risk management.
Ethics Of Computing
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Author : Jacques J. Berleur
language : en
Publisher: Springer Science & Business Media
Release Date : 1996-04-30
Ethics Of Computing written by Jacques J. Berleur and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996-04-30 with Business & Economics categories.
This major reference work represents the first attempt to confront, on a world-wide basis, the way computer associations face up to their own responsibilities in an age increasingly dominated by information and communication technology. The book deals with the codes of ethics and conduct, and related issues. It is the first book to deal with homogenous codes namely codes of national computer societies. Some thirty codes are compared and analysed in depth. To put these into perspective, there are discussion papers covering the methodological, philosophical and organisational issues.