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| artificial intelligence risk management framework: AI-RMF a Practical Guide for NIST AI Risk Management Framework Bobby Jenkins, 2024-05-30 Unlock the Power of Responsible AI with AI-RMF: A PracticalGuide for NIST AI Risk Management Framework.As artificial intelligence (AI) systems become increasinglyintegrated into our daily lives, organizations face the criticalchallenge of managing the associated risks and ensuring thetrustworthy development and deployment of AI technologies.AI-RMF: A Practical Guide is your comprehensive handbook fornavigating the complexities of AI risk management using theNational Institute of Standards and Technology's ArtificialIntelligence Risk Management Framework (AI-RMF).This book offers a deep dive into the AI-RMF, providing step-by-step guidance on implementing this powerful framework acrossvarious industries. You'll explore the history and evolution of AIrisk management, understand the key components of the AI-RMF,and learn practical strategies for applying the framework to yourorganization's unique needs.Whether you're an AI developer, data scientist, securityprofessional, business leader, or system engineer, this book isyour essential guide to operationalizing AI risk management andunlocking the full potential of AI while safeguarding yourorganization and stakeholders. |
| artificial intelligence risk management framework: Trustworthy AI Beena Ammanath, 2022-03-15 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. |
| artificial intelligence risk management framework: The Artificial Intelligence Risk Management Framework Prof Mafor Edwan, 2024-06-30 This compilation of NIST's Artificial Intelligence resources is a student companion to the Artificial Intelligence Risk Management Framework, Trustworthy and Responsible AI Course and serves as a source of information for awareness to non-scholars. It is a great resource for everybody since artificial intelligence is into everybody's business! We are all impacted, somehow! |
| artificial intelligence risk management framework: Powering the Digital Economy: Opportunities and Risks of Artificial Intelligence in Finance El Bachir Boukherouaa, Mr. Ghiath Shabsigh, Khaled AlAjmi, Jose Deodoro, Aquiles Farias, Ebru S Iskender, Mr. Alin T Mirestean, Rangachary Ravikumar, 2021-10-22 This paper discusses the impact of the rapid adoption of artificial intelligence (AI) and machine learning (ML) in the financial sector. It highlights the benefits these technologies bring in terms of financial deepening and efficiency, while raising concerns about its potential in widening the digital divide between advanced and developing economies. The paper advances the discussion on the impact of this technology by distilling and categorizing the unique risks that it could pose to the integrity and stability of the financial system, policy challenges, and potential regulatory approaches. The evolving nature of this technology and its application in finance means that the full extent of its strengths and weaknesses is yet to be fully understood. Given the risk of unexpected pitfalls, countries will need to strengthen prudential oversight. |
| artificial intelligence risk management framework: Introducing MLOps Mark Treveil, Nicolas Omont, Clément Stenac, Kenji Lefevre, Du Phan, Joachim Zentici, Adrien Lavoillotte, Makoto Miyazaki, Lynn Heidmann, 2020-11-30 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 |
| artificial intelligence risk management framework: Disrupting Finance Theo Lynn, John G. Mooney, Pierangelo Rosati, Mark Cummins, 2018-12-06 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. |
| artificial intelligence risk management framework: Risk Management Framework for Fourth Industrial Revolution Technologies Omoseni Oyindamola Adepoju, Nnamdi Ikechi Nwulu, Love Opeyemi David, 2024-10-24 This book focuses on major challenges posed by the Fourth Industrial Revolution (4IR), particularly the associated risks. By recognizing and addressing these risks, it bridges the gap between technological advancements and effective risk management. It further facilitates a swift adoption of technology and equips readers with the knowledge to be cautious during its implementation. Divided into three parts, it covers an overview of 4IR and explores the risks and risk management techniques and comprehensive risk management framework specifically tailored for the 4IR. Features: • Establishes a risk management framework for Industry 4.0 technologies. • Provides a ‘one stop shop’ of different technologies emerging in the Fourth Industrial Revolution. • Follows a consistent structure for each key Industry 4.0 technology in separate chapters. • Details required risk management skills for the technologies of the Fourth Industrial Revolution. • Covers risk monitoring, control, and mitigation measures. This book is aimed at graduate students, technology enthusiasts, and researchers in computer sciences, technology management, business management, and industrial engineering. |
| artificial intelligence risk management framework: Forecasting and Managing Risk in the Health and Safety Sectors Dall’Acqua, Luisa, 2019-02-15 Forecasting new and emerging risks associated with new technologies is a hard and provocative challenge. A wide range of new and modified materials are being made available, and many of these have unknown consequences including nanomaterials, composites, biomaterials, and biocybernetics. Additionally, the greater complexity of man-machine processes and interfaces, the introduction of collaborative robots, and the excessive dependence on computers, as in the case of unmanned vehicles in transportation, could trigger new risks. Forecasting and Managing Risk in the Health and Safety Sectors is an essential reference source that combines theoretical underpinnings with practical relevance in order to introduce training activities to manage uncertainty and risks consequent to emerging technologies. Featuring research on topics such as energy policy, green management, and intelligence cycle, this book is ideally designed for government officials, managers, policymakers, researchers, lecturers, advanced students, and professionals. |
| artificial intelligence risk management framework: The Risk IT Framework Isaca, 2009 |
| artificial intelligence risk management framework: Computational Thinking for Problem Solving and Managerial Mindset Training Dall'Acqua, Luisa, 2021-06-25 The cultural, social, and economic history of mankind is characterized by a succession of needs and problems that have stimulated the invention of operational and conceptual tools to facilitate their solution. The continuous presentation of new needs, an attempt to improve partial solutions to old problems, curiosity, and the disinterested search for knowledge then constituted the fundamental push for scientific, cultural, economic, and social progress. In an increasingly digital society, where software technological tools permeate daily life and, consequently, change the management of reality, mastering of transversal skills is crucial for success. Computational thinking is a set of transversal skills related to the foundations of computer science as a scientific discipline and means a mastering to the process of solving problems. The goal of computational thinking is to acquire interpretative perspectives of reality, which allows one to read the digital experience competently and responsibly. Computational Thinking for Problem Solving and Managerial Mindset Training explores how individuals can be trained into managerial mindsets through computational thinking and computer science. It explores how computer science can be used as a valid guideline to develop skills such as effective soft skills, communication skills, and collaboration. Further, the chapters explore the adoption of computational thinking for individuals to gain managerial mindsets and successfully solve questions and problems in their domain of interest. This will include artificial intelligence applications, strategic thinking, management training, ethics, emergency managerial mindsets, and more. This book is valuable for managers, professionals, practitioners, researchers, academicians, and students interested in how computational thinking can be applied for the training of managerial mindsets. |
| artificial intelligence risk management framework: The Risk IT Practitioner Guide Isaca, 2009 |
| artificial intelligence risk management framework: Artificial Intelligence for Risk Mitigation in the Financial Industry Ambrish Kumar Mishra, Shweta Anand, Narayan C. Debnath, Purvi Pokhariyal, Archana Patel, 2024-07-03 Artificial Intelligence for Risk Mitigation in the Financial Industry This book extensively explores the implementation of AI in the risk mitigation process and provides information for auditing, banking, and financial sectors on how to reduce risk and enhance effective reliability. The applications of the financial industry incorporate vast volumes of structured and unstructured data to gain insight into the financial and non-financial performance of companies. As a result of exponentially increasing data, auditors and management professionals need to enhance processing capabilities while maintaining the effectiveness and reliability of the risk mitigation process. The risk mitigation and audit procedures are processes involving the progression of activities to “transform inputs into output.” As AI systems continue to grow mainstream, it is difficult to imagine an aspect of risk mitigation in the financial industry that will not require AI-related assurance or AI-assisted advisory services. AI can be used as a strong tool in many ways, like the prevention of fraud, money laundering, and cybercrime, detection of risks and probability of NPAs at early stages, sound lending, etc. Audience This is an introductory book that provides insights into the advantages of risk mitigation by the adoption of AI in the financial industry. The subject is not only restricted to individuals like researchers, auditors, and management professionals, but also includes decision-making authorities like the government. This book is a valuable guide to the utilization of AI for risk mitigation and will serve as an important standalone reference for years to come. |
| artificial intelligence risk management framework: Artificial Intelligence David R. Martinez, Bruke M. Kifle, 2024-06-11 The first text to take a systems engineering approach to artificial intelligence (AI), from architecture principles to the development and deployment of AI capabilities. Most books on artificial intelligence (AI) focus on a single functional building block, such as machine learning or human-machine teaming. Artificial Intelligence takes a more holistic approach, addressing AI from the view of systems engineering. The book centers on the people-process-technology triad that is critical to successful development of AI products and services. Development starts with an AI design, based on the AI system architecture, and culminates with successful deployment of the AI capabilities. Directed toward AI developers and operational users, this accessibly written volume of the MIT Lincoln Laboratory Series can also serve as a text for undergraduate seniors and graduate-level students and as a reference book. Key features: In-depth look at modern computing technologies Systems engineering description and means to successfully undertake an AI product or service development through deployment Existing methods for applying machine learning operations (MLOps) AI system architecture including a description of each of the AI pipeline building blocks Challenges and approaches to attend to responsible AI in practice Tools to develop a strategic roadmap and techniques to foster an innovative team environment Multiple use cases that stem from the authors’ MIT classes, as well as from AI practitioners, AI project managers, early-career AI team leaders, technical executives, and entrepreneurs Exercises and Jupyter notebook examples |
| artificial intelligence risk management framework: Artificial Intelligence in Society OECD, 2019-06-11 The artificial intelligence (AI) landscape has evolved significantly from 1950 when Alan Turing first posed the question of whether machines can think. Today, AI is transforming societies and economies. It promises to generate productivity gains, improve well-being and help address global challenges, such as climate change, resource scarcity and health crises. |
| artificial intelligence risk management framework: Artificial Intelligence and Ethics Tarnveer Singh, 2024-11-22 Artificial Intelligence and Ethics is a general and wide-ranging survey of the benefits and ethical dilemmas of artificial intelligence (AI). The rise of AI and super-intelligent AI has created an urgent need to understand the many and varied ethical issues surrounding the technologies and applications of AI. This book lays a path towards the benefits and away from potential risks. It includes over thirty short chapters covering the widest array of topics from generative AI to superintelligence, from regulation to transparency, and from cybersecurity to risk management. Written by an award-winning Chief Information Security Officer (CISO) and experienced Technology Leader with two decades of industry experience, the book includes real-life examples and up-to-date references. The book will be of particular interest to business stakeholders, including executives, scientists, ethicists and policymakers, considering the complexities of AI and how to navigate these. |
| artificial intelligence risk management framework: Artificial Intelligence for Cybersecurity Bojan Kolosnjaji, Huang Xiao, Peng Xu, Apostolis Zarras, 2024-10-31 Gain well-rounded knowledge of AI methods in cybersecurity and obtain hands-on experience in implementing them to bring value to your organization Key Features Familiarize yourself with AI methods and approaches and see how they fit into cybersecurity Learn how to design solutions in cybersecurity that include AI as a key feature Acquire practical AI skills using step-by-step exercises and code examples Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionArtificial intelligence offers data analytics methods that enable us to efficiently recognize patterns in large-scale data. These methods can be applied to various cybersecurity problems, from authentication and the detection of various types of cyberattacks in computer networks to the analysis of malicious executables. Written by a machine learning expert, this book introduces you to the data analytics environment in cybersecurity and shows you where AI methods will fit in your cybersecurity projects. The chapters share an in-depth explanation of the AI methods along with tools that can be used to apply these methods, as well as design and implement AI solutions. You’ll also examine various cybersecurity scenarios where AI methods are applicable, including exercises and code examples that’ll help you effectively apply AI to work on cybersecurity challenges. The book also discusses common pitfalls from real-world applications of AI in cybersecurity issues and teaches you how to tackle them. By the end of this book, you’ll be able to not only recognize where AI methods can be applied, but also design and execute efficient solutions using AI methods.What you will learn Recognize AI as a powerful tool for intelligence analysis of cybersecurity data Explore all the components and workflow of an AI solution Find out how to design an AI-based solution for cybersecurity Discover how to test various AI-based cybersecurity solutions Evaluate your AI solution and describe its advantages to your organization Avoid common pitfalls and difficulties when implementing AI solutions Who this book is for This book is for machine learning practitioners looking to apply their skills to overcome cybersecurity challenges. Cybersecurity workers who want to leverage machine learning methods will also find this book helpful. Fundamental concepts of machine learning and beginner-level knowledge of Python programming are needed to understand the concepts present in this book. Whether you’re a student or an experienced professional, this book offers a unique and valuable learning experience that will enable you to protect your network and data against the ever-evolving threat landscape. |
| artificial intelligence risk management framework: Responsible AI CSIRO, Qinghua Lu, Liming Zhu, Jon Whittle, Xiwei Xu, 2023-12-08 THE FIRST PRACTICAL GUIDE FOR OPERATIONALIZING RESPONSIBLE AI ̃FROM MUL TI°LEVEL GOVERNANCE MECHANISMS TO CONCRETE DESIGN PATTERNS AND SOFTWARE ENGINEERING TECHNIQUES. AI is solving real-world challenges and transforming industries. Yet, there are serious concerns about its ability to behave and make decisions in a responsible way. Operationalizing responsible AI is about providing concrete guidelines to a wide range of decisionmakers and technologists on how to govern, design, and build responsible AI systems. These include governance mechanisms at the industry, organizational, and team level; software engineering best practices; architecture styles and design patterns; system-level techniques connecting code with data and models; and trade-offs in design decisions. Responsible AI includes a set of practices that technologists (for example, technology-conversant decision-makers, software developers, and AI practitioners) can undertake to ensure the AI systems they develop or adopt are trustworthy throughout the entire lifecycle and can be trusted by those who use them. The book offers guidelines and best practices not just for the AI part of a system, but also for the much larger software infrastructure that typically wraps around the AI. First book of its kind to cover the topic of operationalizing responsible AI from the perspective of the entire software development life cycle. Concrete and actionable guidelines throughout the lifecycle of AI systems, including governance mechanisms, process best practices, design patterns, and system engineering techniques. Authors are leading experts in the areas of responsible technology, AI engineering, and software engineering. Reduce the risks of AI adoption, accelerate AI adoption in responsible ways, and translate ethical principles into products, consultancy, and policy impact to support the AI industry. Online repository of patterns, techniques, examples, and playbooks kept up-to-date by the authors. Real world case studies to demonstrate responsible AI in practice. Chart the course to responsible AI excellence, from governance to design, with actionable insights and engineering prowess found in this defi nitive guide. |
| artificial intelligence risk management framework: Secure AI Onboarding Framework Michael Bergman, 2024-08-22 AI Onboarding is the process of fine-tuning generic pre-trained AI models using the transfer learning process and the organisation's proprietary data, such as intellectual property (IP), customer data, and other domain-specific datasets. This fine-tuning transforms a generic AI model into a bespoke business tool that understands organisation-specific terminology, makes decisions in line with internal policies and strategies, and provides insights that are directly relevant to the organisation's goals and challenges. Standing in the way of this powerful transformation is the AI onboarding challenge of protecting the confidentiality, integrity and availability of proprietary data as it is collected, stored, processed and used in fine-tuning. The Secure AI Onboarding Framework is designed to address this challenge by supporting the “Risk Identification” and “Risk treatment” phases of ISO/IEC 27005. It decomposes authoritative resources including the AI Act, OWASP, NIST CSF 2.0, and AI RMF into four critical components, namely Risks, Security Controls, Assessment Questions and Control Implementation Guidance. These components help organisations first, to identify the risks relevant to their AI system and proprietary data, second, define an AI system statement of applicable controls to treat the risks. Thirdly, assess the implementation status of those controls to identify gaps in their readiness to onboard the AI system, and finally, they provide control implementation guidance to facilitate the correct control implementation. Ultimately minimising the security risks related to onboarding AI systems and securely integrating them into their business teams and processes. |
| artificial intelligence risk management framework: Towards Sustainable Artificial Intelligence Ghislain Landry Tsafack Chetsa, 2021-08-14 So far, little effort has been devoted to developing practical approaches on how to develop and deploy AI systems that meet certain standards and principles. This is despite the importance of principles such as privacy, fairness, and social equality taking centre stage in discussions around AI. However, for an organization, failing to meet those standards can give rise to significant lost opportunities. It may further lead to an organization’s demise, as the example of Cambridge Analytica demonstrates. It is, however, possible to pursue a practical approach for the design, development, and deployment of sustainable AI systems that incorporates both business and human values and principles. This book discusses the concept of sustainability in the context of artificial intelligence. In order to help businesses achieve this objective, the author introduces the sustainable artificial intelligence framework (SAIF), designed as a reference guide in the development and deployment of AI systems. The SAIF developed in the book is designed to help decision makers such as policy makers, boards, C-suites, managers, and data scientists create AI systems that meet ethical principles. By focusing on four pillars related to the socio-economic and political impact of AI, the SAIF creates an environment through which an organization learns to understand its risk and exposure to any undesired consequences of AI, and the impact of AI on its ability to create value in the short, medium, and long term. What You Will Learn See the relevance of ethics to the practice of data science and AI Examine the elements that enable AI within an organization Discover the challenges of developing AI systems that meet certain human or specific standards Explore the challenges of AI governance Absorb the key factors to consider when evaluating AI systems Who This Book Is For Decision makers such as government officials, members of the C-suite and other business managers, and data scientists as well as any technology expert aspiring to a data-related leadership role. |
| artificial intelligence risk management framework: Ethics, Governance, and Policies in Artificial Intelligence Luciano Floridi, 2021-11-02 This book offers a synthesis of investigations on the ethics, governance and policies affecting the design, development and deployment of artificial intelligence (AI). Each chapter can be read independently, but the overall structure of the book provides a complementary and detailed understanding of some of the most pressing issues brought about by AI and digital innovation. Given its modular nature, it is a text suitable for readers who wish to gain a reliable orientation about the ethics of AI and for experts who wish to know more about specific areas of the current debate. |
| artificial intelligence risk management framework: Enterprise Risk Management James Lam, 2014-01-06 A fully revised second edition focused on the best practices of enterprise risk management Since the first edition of Enterprise Risk Management: From Incentives to Controls was published a decade ago, much has changed in the worlds of business and finance. That's why James Lam has returned with a new edition of this essential guide. Written to reflect today's dynamic market conditions, the Second Edition of Enterprise Risk Management: From Incentives to Controls clearly puts this discipline in perspective. Engaging and informative, it skillfully examines both the art as well as the science of effective enterprise risk management practices. Along the way, it addresses the key concepts, processes, and tools underlying risk management, and lays out clear strategies to manage what is often a highly complex issue. Offers in-depth insights, practical advice, and real-world case studies that explore the various aspects of ERM Based on risk management expert James Lam's thirty years of experience in this field Discusses how a company should strive for balance between risk and return Failure to properly manage risk continues to plague corporations around the world. Don't let it hurt your organization. Pick up the Second Edition of Enterprise Risk Management: From Incentives to Controls and learn how to meet the enterprise-wide risk management challenge head on, and succeed. |
| artificial intelligence risk management framework: The Artificial Intelligence Imperative Anastassia Lauterbach, Andrea Bonime-Blanc, 2018-04-12 This practical guide to artificial intelligence and its impact on industry dispels common myths and calls for cross-sector, collaborative leadership for the responsible design and embedding of AI in the daily work of businesses and oversight by boards. Artificial intelligence has arrived, and it's coming to a business near you. The disruptive impact of AI on the global economy—from health care to energy, financial services to agriculture, and defense to media—is enormous. Technology literacy is a must for traditional businesses, their boards, policy makers, and governance professionals. This is the first book to explain where AI comes from, why it has emerged as one of the most powerful forces in mergers and acquisitions and research and development, and what companies need to do to implement it successfully. It equips business leaders with a practical roadmap for competing and even thriving in the face of the coming AI revolution. The authors analyze competitive trends, provide industry and governance examples, and explain interactions between AI and other digital technologies, such as blockchain, cybersecurity, and the Internet of Things. At the same time, AI experts will learn how their research and products can increase the competitiveness of their businesses, and corporate boards will come away with a thorough knowledge of the AI governance, ethics, and risk questions to ask. |
| artificial intelligence risk management framework: White Paper on Global Artificial Intelligence Environmental Impact Green AI Institute, U.S.-Asia Sustainable Development Foundation, The rapid growth of AI models and data centers has led to significant environmental impacts, including substantial energy consumption, carbon emissions, and water usage. Data centers now account for approximately 1-2% of global electricity consumption. These growing environmental concerns highlight the urgent need for comprehensive assessment and regulation. This paper introduces the AI Green Index, a framework designed to evaluate the environmental impact of AI models and data centers. The index offers a standardized, quantifiable metric to assess both carbon and water footprints across the life cycle of AI technologies. By promoting transparency, sustainable practices, and carbon-efficient innovations, the Green AI Index aims to guide stakeholders in reducing environmental impacts. Additionally, the paper analyzes current policies in major regions, including China, the U.S., and the EU, providing insights into strengths and weaknesses in environmental governance. The white paper seeks to inform researchers, industry leaders, and policymakers, fostering collaboration toward a sustainable AI future. |
| artificial intelligence risk management framework: Hacking Artificial Intelligence Davey Gibian, 2022-05-05 Sheds light on the ability to hack AI and the technology industry’s lack of effort to secure vulnerabilities. We are accelerating towards the automated future. But this new future brings new risks. It is no surprise that after years of development and recent breakthroughs, artificial intelligence is rapidly transforming businesses, consumer electronics, and the national security landscape. But like all digital technologies, AI can fail and be left vulnerable to hacking. The ability to hack AI and the technology industry’s lack of effort to secure it is thought by experts to be the biggest unaddressed technology issue of our time. Hacking Artificial Intelligence sheds light on these hacking risks, explaining them to those who can make a difference. Today, very few people—including those in influential business and government positions—are aware of the new risks that accompany automated systems. While society hurdles ahead with AI, we are also rushing towards a security and safety nightmare. This book is the first-ever layman’s guide to the new world of hacking AI and introduces the field to thousands of readers who should be aware of these risks. From a security perspective, AI is today where the internet was 30 years ago. It is wide open and can be exploited. Readers from leaders to AI enthusiasts and practitioners alike are shown how AI hacking is a real risk to organizations and are provided with a framework to assess such risks, before problems arise. |
| artificial intelligence risk management framework: Ethical Design of Artificial Intelligence-based Systems for Decision Making Valentina Franzoni, Jordi Vallverdu, Roberto Capobianco, Giulio Biondi, Alfredo Milani, Francesca Alessandra Lisi, Stefano Cagnoni, 2023-11-29 Artificial Intelligence (AI), including Machine Learning with Deep Neural Networks, is making and supporting decisions in ways that increasingly affect humans in many aspects of their lives. Both autonomous and decision-support systems applying AI algorithms and data-driven models are used for decisions about justice, education, physical and psychological health, and to provide or deny access to credit, healthcare, and other essential resources, in all aspects of daily life, in increasingly ubiquitous and sometimes ambiguous ways. Too often these systems are built without considering the human factors associated with their use and the need for clarity about the correct way to use them, and possible biases. Models and systems provide results that are difficult to interpret and are accused of being good or bad, whereas good or bad is only the design of such tools, and the necessary training for them to be properly integrated into human values. |
| artificial intelligence risk management framework: Proceedings of the XVII International symposium Symorg 2020 Dušan Starčević, Sanja Marinković, 2020-06-30 Ever since 1989, the Faculty of Organizational Sciences, University of Belgrade, has been the host of SymOrg, an event that promotes scientific disciplines of organizing and managing a business. Traditionally, the Symposium has been an opportunity for its participants to share and exchange both academic and practical knowledge and experience in a pleasant and creative atmosphere. This time, however, due the challenging situation regarding the COVID-19 pandemic, we have decided that all the essential activities planned for the International Symposium SymOrg 2020 should be carried out online between the 7th and the 9th of September 2020. We are very pleased that the topic of SymOrg 2020, “Business and Artificial Intelligence”, attracted researchers from different institutions, both in Serbia and abroad. Why is artificial intelligence a disruptive technology? Simply because “it significantly alters the way consumers, industries, or businesses operate.” According to the European Commission document titled Artificial Intelligence for Europe 2018, AI is a key disruptive technology that has just begun to reshape the world. The Government of the Republic of Serbia has also recognized the importance of AI for the further development of its economy and society and has prepared an AI Development Strategy for the period between 2020 and 2025. The first step has already been made: the Science Fund of the Republic of Serbia, after a public call, has selected and financed twelve AI projects. This year, more than 200 scholars and practitioners authored and co-authored the 94 scientific and research papers that had been accepted for publication in the Proceedings. All the contributions to the Proceedings are classified into the following 11 sections: Information Systems and Technologies in the Era of Digital Transformation Smart Business Models and Processes Entrepreneurship, Innovation and Sustainable Development Smart Environment for Marketing and Communications Digital Human Resource Management Smart E-Business Quality 4.0 and International Standards Application of Artificial Intelligence in Project Management Digital and Lean Operations Management Transformation of Financial Services Methods and Applications of Data Science in Business and Society We are very grateful to our distinguished keynote speakers: Prof. Moshe Vardi, Rice University, USA, Prof. Blaž Zupan, University of Ljubljana, Slovenia, Prof. Vladan Devedžić, University of Belgrade, Serbia, Milica Đurić-Jovičić, PhD, Director, Science Fund of the Republic of Serbia, and Harri Ketamo, PhD, Founder & Chairman of HeadAI ltd., Finland. Also, special thanks to Prof. Dragan Vukmirović, University of Belgrade, Serbia and Prof. Zoran Ševarac, University of Belgrade, Serbia for organizing workshops in fields of Data Science and Machine Learning and to Prof. Rade Matić, Belgrade Business and Arts Academy of Applied Studies and Milan Dobrota, PhD, CEO at Agremo, Serbia, for their valuable contribution in presenting Serbian experiences in the field of AI. The Faculty of Organizational Sciences would to express its gratitude to the Ministry of Education, Science and Technological Development and all the individuals who have supported and contributed to the organization of the Symposium. We are particularly grateful to the contributors and reviewers who made this issue possible. But above all, we are especially thankful to the authors and presenters for making the SymOrg 2020 a success! |
| artificial intelligence risk management framework: Artificial Intelligence in Drug Development Kavita Sharma, |
| artificial intelligence risk management framework: Advanced Analytics and AI Tony Boobier, 2018-04-03 Be prepared for the arrival of automated decision making Once thought of as science fiction, major corporations are already beginning to use cognitive systems to assist in providing wealth advice and also in medication treatment. The use of Cognitive Analytics/Artificial Intelligence (AI) Systems is set to accelerate, with the expectation that it’ll be considered ‘mainstream’ in the next 5 – 10 years. It’ll change the way we as individuals interact with data and systems—and the way we run our businesses. Cognitive Analysis and AI prepares business users for the era of cognitive analytics / artificial intelligence. Building on established texts and commentary, it specifically prepares you in terms of expectation, impact on personal roles, and responsibilities. It focuses on the specific impact on key industries (retail, financial services, utilities and media) and also on key professions (such as accounting, operational management, supply chain and risk management). Shows you how users interact with the system in natural language Explains how cognitive analysis/AI can source ‘big data’ Provides a roadmap for implementation Gets you up to speed now before you get left behind If you’re a decision maker or budget holder within the corporate context, this invaluable book helps you gain an advantage from the deployment of cognitive analytics tools. |
| artificial intelligence risk management framework: Artificial Intelligence Solutions for Cyber-Physical Systems Pushan Kumar Dutta, Pethuru Raj, B. Sundaravadivazhagan, CHITHIRAI PON Selvan, 2024-09-16 Smart manufacturing environments are revolutionizing the industrial sector by integrating advanced technologies, such as the Internet of Things (IoT), artificial intelligence (AI), and robotics, to achieve higher levels of efficiency, productivity, and safety. However, the increasing complexity and interconnectedness of these systems also introduce new security challenges that must be addressed to ensure the safety of human workers and the integrity of manufacturing processes. Key topics include risk assessment methodologies, secure communication protocols, and the development of standard specifications to guide the design and implementation of HCPS. Recent research highlights the importance of adopting a multi-layered approach to security, encompassing physical, network, and application layers. Furthermore, the integration of AI and machine learning techniques enables real-time monitoring and analysis of system vulnerabilities, as well as the development of adaptive security measures. Artificial Intelligence Solutions for Cyber-Physical Systems discusses such best practices and frameworks as NIST Cybersecurity Framework, ISO/IEC 27001, and IEC 62443 of advanced technologies. It presents strategies and methods to mitigate risks and enhance security, including cybersecurity frameworks, secure communication protocols, and access control measures. The book also focuses on the design, implementation, and management of secure HCPS in smart manufacturing environments. It covers a wide range of topics, including risk assessment, security architecture, data privacy, and standard specifications, for HCPS. The book highlights the importance of securing communication protocols, the role of artificial intelligence and machine learning in threat detection and mitigation, and the need for robust cybersecurity frameworks in the context of smart manufacturing. |
| artificial intelligence risk management framework: Systems, Software and Services Process Improvement Murat Yilmaz, |
| artificial intelligence risk management framework: Research Handbook on Human Resource Management and Disruptive Technologies Tanya Bondarouk, Jeroen Meijerink, 2024-03-14 This comprehensive and judicious Research Handbook examines the fundamental influence of the emergence of contemporary disruptive technologies, including artificial intelligence, online platforms, the internet of things, and social robots, on Human Resource Management (HRM). |
| artificial intelligence risk management framework: Using Traditional Design Methods to Enhance AI-Driven Decision Making Nguyen, Tien V. T., Vo, Nhut T. M., 2024-01-10 In the rapidly evolving landscape of industrial activities, artificial intelligence (AI) has emerged as a powerful force driving transformative change. Among its many applications, AI has proven to be instrumental in reducing processing costs associated with optimization challenges. The intersection of AI with optimization and multi-criteria decision making (MCDM) techniques has led to practical solutions in diverse fields such as manufacturing, transportation, finance, economics, and artificial intelligence. Using Traditional Design Methods to Enhance AI-Driven Decision Making delves into a wide array of topics related to optimization, decision-making, and their applications. Drawing on foundational contributions, system developments, and innovative techniques, the book explores the synergy between traditional design methods and AI-driven decision-making approaches. The book is ideal for higher education faculty and administrators, students of higher education, librarians, researchers, graduate students, and academicians. Contributors are invited to explore a wide range of topics, including the role of AI-driven decision-making in leadership, trends in AI-driven decision-making in Industry 5.0, applications in various industries such as manufacturing, transportation, healthcare, and banking services, as well as AI-driven optimization in mechanical engineering and materials. |
| artificial intelligence risk management framework: Network Security Empowered by Artificial Intelligence Yingying Chen, |
| artificial intelligence risk management framework: Artificial Intelligence In Medicine: A Practical Guide For Clinicians Campion Quinn, 2024-02-06 'Artificial Intelligence in Medicine' is a comprehensive guide exploring the transformative impact of artificial intelligence (AI) in healthcare. The book delves into the foundational concepts and historical development of AI in medicine, highlighting data collection, preprocessing, and feature extraction crucial for medical applications. It showcases the benefits of AI, such as accurate diagnoses and personalized treatments, while addressing ethical and regulatory considerations.The book examines the practical aspects of AI implementation in clinical practice and emphasizes the human aspect of AI in healthcare and patient engagement. Readers can gain insights into the role of AI in clinical decision support, collaborative learning, and knowledge sharing. It concludes with a glimpse into the future of AI-driven healthcare, exploring the emerging technologies and trends in the rapidly evolving field of AI in medicine. |
| artificial intelligence risk management framework: Towards a Knowledge-Aware AI A. Dimou, S. Neumaier, T. Pellegrini, 2022-09-29 Semantic systems lie at the heart of modern computing, interlinking with areas as diverse as AI, data science, knowledge discovery and management, big data analytics, e-commerce, enterprise search, technical documentation, document management, business intelligence, enterprise vocabulary management, machine learning, logic programming, content engineering, social computing, and the Semantic Web. This book presents the proceedings of SEMANTiCS 2022, the 18th International Conference on Semantic Systems, held as a hybrid event – live in Vienna, Austria and online – from 12 to 15 September 2022. The SEMANTiCS conference is an annual meeting place for the professionals and researchers who make semantic computing work, who understand its benefits and encounter its limitations, and is attended by information managers, IT architects, software engineers, and researchers from organizations ranging from research facilities and NPOs, through public administrations to the largest companies in the world. The theme and subtitle of the 2022 conference was Towards A Knowledge-Aware AI, and the book contains 15 papers, selected on the basis of quality, impact and scientific merit following a rigorous review process which resulted in an acceptance rate of 29%. The book is divided into four chapters: semantics in data quality, standards and protection; representation learning and reasoning for downstream AI tasks; ontology development; and learning over complementary knowledge. Providing an overview of emerging trends and topics in the wide area of semantic computing, the book will be of interest to anyone involved in the development and deployment of computer technology and AI systems. |
| artificial intelligence risk management framework: AI for good: India and beyond Maneesha Dhir, Sonal Verma, 2024-05-15 AI FOR GOOD-INDIA AND BEYOND is a seminal work offering a comprehensive navigation into the evolution and current state of AI regulation in India, marking significant judicial decisions and emerging policies with a keen eye on their alignment with international laws/standards. The book advocates for a Human Rights-Centric Policy Approach promoting fairness, accountability, and transparency in the development of ethical AI systems. Analysing global trends and legal approaches towards AI governance, the authors provide a comparative panorama spanning the latest EU AI Act (2024) to enactments in Brazil, China, Japan, and the USA. Key features • Comprehensive Analysis: Detailed analysis of AI & laws, and policies in India and their global interplay. • Legal Frameworks: Exploration of the statutes and case laws that govern AI, highlighting the evolving legal landscape with real-life examples. • Ethical Considerations: Discussion on the ethical frameworks that must be considered for responsible AI management, including safety, inclusivity, equality, privacy, transparency, accountability, and protection of human values. • Policy Recommendations: Tailored recommendations for India, considering its unique position in the global market and potential for AI leadership. • International Perspectives: Examination of international frameworks and guidelines from major entities like the EU, OECD, and the UNESCO, offering a global context for comparison. • IPR and AI Interplay: A dedicated section on the relationship between AI and intellectual property rights, addressing concerns around AI-generated content, and ownership. • Civil and Criminal Liabilities: Insights into the complex issues of AI and legal liability, highlighting discussions of potential civil and criminal implications. • Legal Personhood of AI: An in-depth look at the concept of granting legal personhood to AI entities and the related legal and ethical implications. • Data Governance: The draft National Data Governance Framework Policy and its importance for managing government data and fostering an ecosystem for AI are covered. • Deeper Insights : 500 plus references for deeper understanding of the topics illustrated in the book |
| artificial intelligence risk management framework: Artificial Intelligence and Islamic Finance Adel M. Sarea, Ahmed H. Elsayed, Saeed A. Bin-Nashwan, 2021-12-31 This book provides a systematic overview of the current trends in research relating to the use of artificial intelligence in Islamic financial institutions (IFIs), across all organization of Islamic cooperation (OIC) countries. Artificial Intelligence and Islamic Finance discusses current and potential applications of artificial intelligence (AI) for risk management in Islamic finance. It covers various techniques of risk management, encompassing asset and liability management risk, credit, market, operational, liquidity risk, as well as regulatory and Shariah risk compliance within the financial industry. The authors highlight AI’s ability to combat financial crime such as monitoring trader recklessness, anti-fraud and anti-money laundering, and assert that the capacity of machine learning (ML) to examine large amounts of data allows for greater granular and profound analyses across a variety of Islamic financial products and services. The book concludes with practical limitations around data management policies, transparency, and lack of necessary skill sets within financial institutions. By adopting new methodological approaches steeped in an Islamic economic framework (e.g., analysing FinTech in the context of Shariah principles and Islamic values), it devises practical solutions and generates insightful knowledge, helping readers to understand and explore the role of technological enablers in the Islamic finance industry, such as RegTech and artificial intelligence, in providing better and Shariah-compliant services to customers through digital platforms. The book will attract a wide readership spanning Shariah scholars, academicians, and researchers as well as Islamic financial practitioners and policy makers. |
| artificial intelligence risk management framework: Enterprise Risk Management in Today’s World Jean-Paul Louisot, 2024-10-28 Enterprise Risk Management in Today’s World examines enterprise risk management in its past, present and future, exploring the role that directors and leaders in organizations have in devising risk management strategies, analysing values such as trust, resilience, CSR and governance within organizations. |
| artificial intelligence risk management framework: Frontiers of Artificial Intelligence, Ethics, and Multidisciplinary Applications Mina Farmanbar, |
| artificial intelligence risk management framework: Interpretable Machine Learning Christoph Molnar, 2020 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. |
AI Risk Management Framework | NIST - National Institute of …
The NIST AI Risk Management Framework (AI RMF) is intended for voluntary use and to improve the ability to incorporate trustworthiness considerations into the design, development, use, and …
Artificial Intelligence Risk Management Framework (AI …
As directed by the National Artificial Intelligence Initiative Act of 2020(P.L. 116-283), the goal of the AI RMF is to offer a resource to the organizations designing, developing, deploying, or …
Artificial Intelligence Risk Management Framework - Federal Register
Jul 29, 2021 · The NIST Artificial Intelligence Risk Management Framework (AI RMF or Framework) is intended for voluntary use and to improve the ability to incorporate …
AI Risk Management Framework | NIST - data.aclum.org
In collaboration with the private and public sectors, NIST has developed a framework to better manage risks to individuals, organizations, and society associated with artificial intelligence (AI).
Artificial Intelligence Risk Management Framework (AI RMF) …
Feb 17, 2023 · This is where the Artificial Intelligence Risk Management Framework 1.0 (AI RMF 1.0) from the National Institute of Standards and Technology comes in. This framework …
Understanding NIST’s AI Risk Management Framework - PLI
Apr 26, 2023 · NIST’s definition: An engineered or machine-based system that can, for a given set of objectives, generate outputs such as predictions, recommendations, or decisions influencing …
A Checklist for the NIST AI Risk Management Framework
Oct 18, 2024 · The NIST AI Risk Management Framework was created to offer a voluntary guide for organizations aiming to improve their management of AI-related risks. It helps simplify …
NIST Artificial Intelligence Risk Management Framework
Higher consequences mean more effort is required to prevent the corresponding risk events. Understanding consequences helps you prioritize the risks so you can work on those of most …
AI Risk Management Framework - csrc.nist.rip
Trustworthy AI systems should achieve a high degree of control over risk while retaining a high level of performance quality. Achieving this difficult goal requires a comprehensive approach to …
Artificial Intelligence Risk Management Framework (AI RMF 1.0)
Jan 26, 2023 · As directed by the National Artificial Intelligence Initiative Act of 2020 (P.L. 116-283), the goal of the AI RMF is to offer a resource to the organizations designing, developing, …
AI Risk Management Framework | NIST - National Institute of …
The NIST AI Risk Management Framework (AI RMF) is intended for voluntary use and to improve the ability to incorporate trustworthiness considerations into the design, development, use, and …
Artificial Intelligence Risk Management Framework (AI …
As directed by the National Artificial Intelligence Initiative Act of 2020(P.L. 116-283), the goal of the AI RMF is to offer a resource to the organizations designing, developing, deploying, or …
Artificial Intelligence Risk Management Framework - Federal Register
Jul 29, 2021 · The NIST Artificial Intelligence Risk Management Framework (AI RMF or Framework) is intended for voluntary use and to improve the ability to incorporate …
AI Risk Management Framework | NIST - data.aclum.org
In collaboration with the private and public sectors, NIST has developed a framework to better manage risks to individuals, organizations, and society associated with artificial intelligence (AI).
Artificial Intelligence Risk Management Framework (AI RMF) …
Feb 17, 2023 · This is where the Artificial Intelligence Risk Management Framework 1.0 (AI RMF 1.0) from the National Institute of Standards and Technology comes in. This framework …
Understanding NIST’s AI Risk Management Framework - PLI
Apr 26, 2023 · NIST’s definition: An engineered or machine-based system that can, for a given set of objectives, generate outputs such as predictions, recommendations, or decisions influencing …
A Checklist for the NIST AI Risk Management Framework
Oct 18, 2024 · The NIST AI Risk Management Framework was created to offer a voluntary guide for organizations aiming to improve their management of AI-related risks. It helps simplify …
NIST Artificial Intelligence Risk Management Framework
Higher consequences mean more effort is required to prevent the corresponding risk events. Understanding consequences helps you prioritize the risks so you can work on those of most …
AI Risk Management Framework - csrc.nist.rip
Trustworthy AI systems should achieve a high degree of control over risk while retaining a high level of performance quality. Achieving this difficult goal requires a comprehensive approach to …
Artificial Intelligence Risk Management Framework (AI RMF 1.0)
Jan 26, 2023 · As directed by the National Artificial Intelligence Initiative Act of 2020 (P.L. 116-283), the goal of the AI RMF is to offer a resource to the organizations designing, developing, …
