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AI and Risk Management: A Comprehensive Guide
Author: Dr. Anya Sharma, PhD, Certified Risk Management Assurance (CRMA), with 15 years of experience in financial risk management and 5 years specializing in the application of AI in risk mitigation strategies for Fortune 500 companies.
Publisher: Risk Management Institute (RMI), a leading global provider of risk management education, research, and consulting services, specializing in emerging technologies and their impact on risk landscapes.
Editor: Mr. David Chen, Certified Information Systems Auditor (CISA), with 10 years of experience in cybersecurity risk management and a focus on AI-driven security solutions.
Summary: This guide explores the transformative impact of Artificial Intelligence (AI) on risk management. It examines the opportunities AI presents for enhanced risk identification, assessment, and mitigation, while also highlighting the unique risks associated with deploying AI systems. We delve into best practices for responsible AI implementation, addressing ethical concerns, data biases, and explainability challenges. The guide provides practical strategies for navigating the complex landscape of AI and risk management, ensuring a secure and effective integration of AI into your organization's risk framework.
Keywords: AI and risk management, AI risk management, artificial intelligence risk, AI ethics, data bias in AI, explainable AI (XAI), AI security, responsible AI, AI governance, AI regulation
1. Introduction: The Intersection of AI and Risk Management
The rapid advancement of Artificial Intelligence (AI) presents both immense opportunities and significant challenges for organizations. While AI offers powerful tools to enhance risk management processes, its deployment also introduces new and complex risks. This guide will explore this dynamic interplay, providing a comprehensive overview of AI and risk management best practices, pitfalls, and strategies for responsible implementation. The integration of AI and risk management is no longer a futuristic concept; it’s a present-day necessity for organizations striving to remain competitive and resilient in an increasingly complex world.
2. Opportunities: Leveraging AI for Enhanced Risk Management
AI offers several advantages in risk management:
Improved Risk Identification: AI algorithms can analyze vast datasets to identify patterns and anomalies indicative of emerging risks, often far exceeding human capabilities. This proactive approach allows for early intervention and mitigation.
Enhanced Risk Assessment: AI-powered systems can assess the likelihood and impact of risks with greater speed and accuracy than traditional methods, enabling more informed decision-making.
Optimized Risk Mitigation: AI can automate risk mitigation processes, such as fraud detection, cybersecurity threat response, and regulatory compliance monitoring, freeing up human resources for more strategic tasks.
Predictive Risk Modeling: AI algorithms can build sophisticated predictive models to forecast future risks, enabling organizations to proactively prepare for potential disruptions.
3. Challenges: Navigating the Risks of AI in Risk Management
Despite its benefits, integrating AI into risk management also presents significant challenges:
Data Bias and Fairness: AI algorithms are trained on data, and if this data reflects existing biases, the AI system will perpetuate and potentially amplify those biases in its risk assessments.
Lack of Explainability (Black Box Problem): Many AI algorithms operate as "black boxes," making it difficult to understand how they arrive at their conclusions. This lack of transparency can hinder trust and accountability.
AI Security Risks: AI systems themselves can be vulnerable to cyberattacks, data breaches, and adversarial attacks, potentially compromising the integrity of risk management processes.
Regulatory Uncertainty: The regulatory landscape for AI is still evolving, creating uncertainty for organizations about compliance requirements.
Ethical Considerations: The use of AI in risk management raises ethical concerns, particularly regarding privacy, surveillance, and algorithmic accountability.
4. Best Practices for Responsible AI Implementation in Risk Management
To effectively manage the risks associated with AI, organizations should adopt the following best practices:
Establish a Robust AI Governance Framework: Develop clear policies, procedures, and guidelines for the ethical and responsible development, deployment, and monitoring of AI systems.
Prioritize Data Quality and Bias Mitigation: Ensure that the data used to train AI algorithms is accurate, representative, and free from bias. Employ techniques to detect and mitigate bias throughout the AI lifecycle.
Focus on Explainability and Transparency: Select AI algorithms that are transparent and provide explanations for their outputs. Implement techniques such as Explainable AI (XAI) to enhance understanding and trust.
Implement Strong Security Measures: Protect AI systems from cyberattacks and data breaches through robust security protocols and regular vulnerability assessments.
Foster Collaboration and Communication: Encourage collaboration between data scientists, risk managers, and other stakeholders to ensure a holistic and responsible approach to AI implementation.
Continuously Monitor and Evaluate: Regularly monitor the performance of AI systems and evaluate their impact on risk management processes. Adapt and refine strategies as needed.
5. Case Studies: Real-World Examples of AI in Risk Management
(This section would include several real-world examples of how companies have successfully implemented AI in risk management, showcasing both successes and failures. Examples could include fraud detection in finance, predictive maintenance in manufacturing, or cybersecurity threat detection in IT.)
6. Future Trends in AI and Risk Management
Future trends will likely involve the increased use of advanced AI techniques such as deep learning and reinforcement learning for risk modeling and prediction. Increased focus on ethical AI, explainability, and regulatory compliance will also shape the future of AI and risk management.
7. Conclusion
The integration of AI and risk management presents both significant opportunities and considerable challenges. By adopting a proactive and responsible approach, focusing on best practices, and addressing the ethical considerations, organizations can harness the power of AI to enhance their risk management capabilities while mitigating the associated risks. The journey of integrating AI into risk management is ongoing, demanding continuous learning, adaptation, and a commitment to responsible innovation.
FAQs
1. What are the biggest risks associated with using AI in risk management? The biggest risks include data bias, lack of explainability, AI security vulnerabilities, and regulatory uncertainty.
2. How can organizations ensure the ethical use of AI in risk management? Organizations should establish clear ethical guidelines, prioritize data fairness, promote transparency, and involve stakeholders in decision-making processes.
3. What are the key benefits of using AI for risk identification? AI can analyze massive datasets to identify subtle patterns and anomalies indicative of emerging risks, far exceeding human capabilities.
4. How can organizations address the “black box” problem in AI risk management? By prioritizing explainable AI (XAI) techniques, selecting transparent algorithms, and documenting decision-making processes.
5. What role does data quality play in AI-driven risk management? Data quality is paramount. Biased or inaccurate data will lead to biased and unreliable risk assessments.
6. How can organizations prepare for the regulatory landscape surrounding AI in risk management? By staying informed about evolving regulations, actively participating in industry discussions, and building a compliance-focused culture.
7. What are some practical steps organizations can take to implement AI in risk management? Start with a pilot project focusing on a specific risk area, gradually scaling up as experience and confidence grow.
8. How can organizations measure the effectiveness of AI in their risk management processes? By tracking key metrics such as the accuracy of risk predictions, the efficiency of mitigation processes, and the reduction in overall risk exposure.
9. What are the long-term implications of AI in risk management? AI will likely become increasingly integrated into risk management, transforming how organizations identify, assess, and mitigate risks.
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2. "Mitigating Bias in AI-Powered Risk Assessments": Focuses on techniques for identifying and mitigating bias in AI algorithms used for risk assessment.
3. "Explainable AI (XAI) in Risk Management": Discusses the importance of XAI and provides examples of techniques to make AI-driven risk assessments more transparent.
4. "The Role of AI in Cybersecurity Risk Management": Examines how AI is used to enhance cybersecurity defenses and detect threats.
5. "AI and Regulatory Compliance in Financial Risk Management": Discusses the challenges and opportunities of using AI to ensure compliance with financial regulations.
6. "AI-Powered Predictive Maintenance for Risk Mitigation": Explores the use of AI in predictive maintenance to reduce equipment failures and operational risks.
7. "Ethical Considerations in AI-Driven Risk Management": Delves into the ethical implications of using AI in risk management, including privacy and accountability.
8. "Building a Robust AI Governance Framework for Risk Management": Provides a step-by-step guide to establishing a comprehensive governance framework for AI in risk management.
9. "The Future of AI and Risk Management: Emerging Trends and Challenges": Looks ahead at the future trends and challenges that lie ahead for AI in risk management.
| ai and risk management: 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. |
| ai and risk management: Artificial Intelligence for Risk Management Archie Addo, Srini Centhala, Muthu Shanmugam, 2020-03-13 Artificial Intelligence (AI) for Risk Management is about using AI to manage risk in the corporate environment. The content of this work focuses on concepts, principles, and practical applications that are relevant to the corporate and technology environments. The authors introduce AI and discuss the different types, capabilities, and purposes–including challenges. With AI also comes risk. This book defines risk, provides examples, and includes information on the risk-management process. Having a solid knowledge base for an AI project is key and this book will help readers define the knowledge base needed for an AI project by developing and identifying objectives of the risk-knowledge base and knowledge acquisition for risk. This book will help you become a contributor on an AI team and learn how to tell a compelling story with AI to drive business action on risk. |
| ai and risk management: 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. |
| ai and risk management: Artificial Intelligence in Financial Markets Christian L. Dunis, Peter W. Middleton, Andreas Karathanasopolous, Konstantinos Theofilatos, 2016-11-21 As technology advancement has increased, so to have computational applications for forecasting, modelling and trading financial markets and information, and practitioners are finding ever more complex solutions to financial challenges. Neural networking is a highly effective, trainable algorithmic approach which emulates certain aspects of human brain functions, and is used extensively in financial forecasting allowing for quick investment decision making. This book presents the most cutting-edge artificial intelligence (AI)/neural networking applications for markets, assets and other areas of finance. Split into four sections, the book first explores time series analysis for forecasting and trading across a range of assets, including derivatives, exchange traded funds, debt and equity instruments. This section will focus on pattern recognition, market timing models, forecasting and trading of financial time series. Section II provides insights into macro and microeconomics and how AI techniques could be used to better understand and predict economic variables. Section III focuses on corporate finance and credit analysis providing an insight into corporate structures and credit, and establishing a relationship between financial statement analysis and the influence of various financial scenarios. Section IV focuses on portfolio management, exploring applications for portfolio theory, asset allocation and optimization. This book also provides some of the latest research in the field of artificial intelligence and finance, and provides in-depth analysis and highly applicable tools and techniques for practitioners and researchers in this field. |
| ai and risk management: Artificial Intelligence Design and Solution for Risk and Security Archie Addo, Srini Centhala, Muthu Shanmugam, 2020-03-13 Artificial Intelligence (AI) Design and Solutions for Risk and Security targets readers to understand, learn, define problems, and architect AI projects. Starting from current business architectures and business processes to futuristic architectures. Introduction to data analytics and life cycle includes data discovery, data preparation, data processing steps, model building, and operationalization are explained in detail. The authors examine the AI and ML algorithms in detail, which enables the readers to choose appropriate algorithms during designing solutions. Functional domains and industrial domains are also explained in detail. The takeaways are learning and applying designs and solutions to AI projects with risk and security implementation and knowledge about futuristic AI in five to ten years. |
| ai and risk management: The Future of Risk Management Howard Kunreuther, Robert J. Meyer, Erwann O. Michel-Kerjan, 2019-07-26 Whether man-made or naturally occurring, large-scale disasters can cause fatalities and injuries, devastate property and communities, savage the environment, impose significant financial burdens on individuals and firms, and test political leadership. Moreover, global challenges such as climate change and terrorism reveal the interdependent and interconnected nature of our current moment: what occurs in one nation or geographical region is likely to have effects across the globe. Our information age creates new and more integrated forms of communication that incur risks that are difficult to evaluate, let alone anticipate. All of this makes clear that innovative approaches to assessing and managing risk are urgently required. When catastrophic risk management was in its inception thirty years ago, scientists and engineers would provide estimates of the probability of specific types of accidents and their potential consequences. Economists would then propose risk management policies based on those experts' estimates with little thought as to how this data would be used by interested parties. Today, however, the disciplines of finance, geography, history, insurance, marketing, political science, sociology, and the decision sciences combine scientific knowledge on risk assessment with a better appreciation for the importance of improving individual and collective decision-making processes. The essays in this volume highlight past research, recent discoveries, and open questions written by leading thinkers in risk management and behavioral sciences. The Future of Risk Management provides scholars, businesses, civil servants, and the concerned public tools for making more informed decisions and developing long-term strategies for reducing future losses from potentially catastrophic events. Contributors: Mona Ahmadiani, Joshua D. Baker, W. J. Wouter Botzen, Cary Coglianese, Gregory Colson, Jeffrey Czajkowski, Nate Dieckmann, Robin Dillon, Baruch Fischhoff, Jeffrey A. Friedman, Robin Gregory, Robert W. Klein, Carolyn Kousky, Howard Kunreuther, Craig E. Landry, Barbara Mellers, Robert J. Meyer, Erwann Michel-Kerjan, Robert Muir-Wood, Mark Pauly, Lisa Robinson, Adam Rose, Paul J. H. Schoemaker, Paul Slovic, Phil Tetlock, Daniel Västfjäll, W. Kip Viscusi, Elke U. Weber, Richard Zeckhauser. |
| ai and risk management: Risk Modeling Terisa Roberts, Stephen J. Tonna, 2022-09-20 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. |
| ai and risk management: Machine Learning for Financial Risk Management with Python Abdullah Karasan, 2021-12-07 Financial risk management is quickly evolving with the help of artificial intelligence. With this practical book, developers, programmers, engineers, financial analysts, risk analysts, and quantitative and algorithmic analysts will examine Python-based machine learning and deep learning models for assessing financial risk. Building hands-on AI-based financial modeling skills, you'll learn how to replace traditional financial risk models with ML models. Author Abdullah Karasan helps you explore the theory behind financial risk modeling before diving into practical ways of employing ML models in modeling financial risk using Python. With this book, you will: Review classical time series applications and compare them with deep learning models Explore volatility modeling to measure degrees of risk, using support vector regression, neural networks, and deep learning Improve market risk models (VaR and ES) using ML techniques and including liquidity dimension Develop a credit risk analysis using clustering and Bayesian approaches Capture different aspects of liquidity risk with a Gaussian mixture model and Copula model Use machine learning models for fraud detection Predict stock price crash and identify its determinants using machine learning models |
| ai and risk management: Patient Safety Ethics John D. Banja, 2019-06-25 Developing best practices and ethical systems to protect and enhance patient safety. Human errors occur all too frequently in medical practice settings. One sobering recent report claimed that medical errors are the third leading cause of death in the United States. Hoping to reverse this disturbing trend but wondering why it is that things usually go well despite errors, John D. Banja's Patient Safety Ethics lays out a model that advocates vigilance, mindfulness, compliance, and humility as core ethical principles of patient safety. Arguing that the safe provision of healthcare is one of the most fundamental moral obligations of clinicians, Banja surveys the research literature on harm-causing medical errors to explore the ethical foundations of patient safety and to reduce the severity and frequency of medical error. Drawing on contemporary scholarship on quality improvement, risk management, and medical decision making, Banja also relies on a novel source of information to illustrate patient safety ethics: medical malpractice suits. Providing professional perspective with insights from prominent patient safety experts, Patient Safety Ethics identifies hazard pitfalls and suggests concrete ways for clinicians and regulators to improve patient safety through an ethically cultivated program of hazard awareness. |
| ai and risk management: Economics and Law of Artificial Intelligence Georgios I. Zekos, 2021-01-11 This book presents a comprehensive analysis of the alterations and problems caused by new technologies in all fields of the global digital economy. The impact of artificial intelligence (AI) not only on law but also on economics is examined. In the first part, the economics of AI are explored, including topics such as e-globalization and digital economy, corporate governance, risk management, and risk development, followed by a quantitative econometric analysis which utilizes regressions stipulating the scale of the impact. In the second part, the author presents the law of AI, covering topics such as the law of electronic technology, legal issues, AI and intellectual property rights, and legalizing AI. Case studies from different countries are presented, as well as a specific analysis of international law and common law. This book is a must-read for scholars and students of law, economics, and business, as well as policy-makers and practitioners, interested in a better understanding of legal and economic aspects and issues of AI and how to deal with them. |
| ai and risk management: 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. |
| ai and risk management: Artificial Intelligence for Managers Malay A. Upadhyay, 2020-09-17 Understand how to adopt and implement AI in your organization Key Features _ 7 Principles of an AI Journey _ The TUSCANE Approach to Become Data Ready _ The FAB-4 Model to Choose the Right AI Solution _ Major AI Techniques & their Applications: - CART & Ensemble Learning - Clustering, Association Rules & Search - Reinforcement Learning - Natural Language Processing - Image Recognition Description Most AI initiatives in organizations fail today not because of a lack of good AI solutions, but because of a lack of understanding of AI among its end users, decision makers and investors. Today, organizations need managers who can leverage AI to solve business problems and provide a competitive advantage. This book is designed to enable you to fill that need, and create an edge for your career. The chapters offer unique managerial frameworks to guide an organization's AI journey. The first section looks at what AI is; and how you can prepare for it, decide when to use it, and avoid pitfalls on the way. The second section dives into the different AI techniques and shows you where to apply them in business. The final section then prepares you from a strategic AI leadership perspective to lead the future of organizations. By the end of the book, you will be ready to offer any organization the capability to use AI successfully and responsibly - a need that is fast becoming a necessity. What will you learn _ Understand the major AI techniques & how they are used in business. _ Determine which AI technique(s) can solve your business problem. _ Decide whether to build or buy an AI solution. _ Estimate the financial value of an AI solution or company. _ Frame a robust policy to guide the responsible use of AI. Who this book is for This book is for Executives, Managers and Students on both Business and Technical teams who would like to use Artificial Intelligence effectively to solve business problems or get an edge in their careers. Table of Contents 1.Preface 2.Acknowledgement 3.About the Author 4.Section 1: Beginning an AI Journey a. AI Fundamentals b. 7 Principles of an AI Journey c. Getting Ready to Use AI 5.Section 2: Choosing the Right AI Techniques a. Inside the AI Laboratory b. How AI Predicts Values & Categories c. How AI Understands and Predicts Behaviors & Scenarios d. How AI Communicates & Learns from Mistakes e. How AI Starts to Think Like Humans 6.Section 3: Using AI Successfully & Responsibly a. AI Adoption & Valuation b. AI Strategy, Policy & Risk Management 7.Epilogue |
| ai and risk management: 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. |
| ai and risk management: Handbook of Research on Applied AI for International Business and Marketing Applications Christiansen, Bryan, Škrinjari?, Tihana, 2020-09-25 Artificial intelligence (AI) describes machines/computers that mimic cognitive functions that humans associate with other human minds, such as learning and problem solving. As businesses have evolved to include more automation of processes, it has become more vital to understand AI and its various applications. Additionally, it is important for workers in the marketing industry to understand how to coincide with and utilize these techniques to enhance and make their work more efficient. The Handbook of Research on Applied AI for International Business and Marketing Applications is a critical scholarly publication that provides comprehensive research on artificial intelligence applications within the context of international business. Highlighting a wide range of topics such as diversification, risk management, and artificial intelligence, this book is ideal for marketers, business professionals, academicians, practitioners, researchers, and students. |
| ai and risk management: 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. |
| ai and risk management: Keeping Your AI Under Control Anand Tamboli, 2019-11-09 Much of our daily lives intertwine with artificial intelligence. From watching movies recommended by our entertainment streaming service, to interacting with customer service chatbots, to autotagging photos of friends in our social media apps, AI plays an invisible part in enriching our lives. While AI may be seen as a panacea for enterprise advancement and consumer convenience, it is still an emerging technology, and its explosive growth needs to be approached with proper care and preparation. How do we tackle the challenges it presents, and how do we make sure that it does precisely what it is supposed to do? In Keeping Your AI Under Control, author Anand Tamboli explores the inherent risk factors of the widespread implementation of artificial intelligence. The author delves into several real-life case studies of AI gone wrong, including Microsoft’s 2016 chatbot disaster, Uber’s autonomous vehicle fatally wounding a pedestrian, and an entire smart home in Germany dangerously malfunctioning because of one bad lightbulb. He expertly addresses the need to challenge our current assumptions about the infallibility of technology. The importance of data governance, rigorous testing before roll-out, a chain of human accountability, ethics, and much more are all detailed in Keeping Your AI Under Control. Artificial intelligence will not solve all of our problems for good, but it can (and will) present us with new solutions. These solutions can only be achieved with proper planning, continued maintenance, and above all, a foundation of attuned human supervision. What You Will Learn Understand various types of risks involved in developing and using AI solutionsIdentify, evaluate, and quantify risks pragmatically Utilize AI insurance to support residual risk management Who This Book Is For Progressive businesses that are on a journey to use AI (buyers/customers), technical and financial leaders in AI solution companies (solution vendors), AI system integrators (intermediaries), project and technology leads of AI deployment projects, technology purchase decision makers, CXOs and legal officers (solution users). |
| ai and risk management: A Human's Guide to Machine Intelligence Kartik Hosanagar, 2020-03-10 A Wharton professor and tech entrepreneur examines how algorithms and artificial intelligence are starting to run every aspect of our lives, and how we can shape the way they impact us Through the technology embedded in almost every major tech platform and every web-enabled device, algorithms and the artificial intelligence that underlies them make a staggering number of everyday decisions for us, from what products we buy, to where we decide to eat, to how we consume our news, to whom we date, and how we find a job. We've even delegated life-and-death decisions to algorithms--decisions once made by doctors, pilots, and judges. In his new book, Kartik Hosanagar surveys the brave new world of algorithmic decision-making and reveals the potentially dangerous biases they can give rise to as they increasingly run our lives. He makes the compelling case that we need to arm ourselves with a better, deeper, more nuanced understanding of the phenomenon of algorithmic thinking. And he gives us a route in, pointing out that algorithms often think a lot like their creators--that is, like you and me. Hosanagar draws on his experiences designing algorithms professionally--as well as on history, computer science, and psychology--to explore how algorithms work and why they occasionally go rogue, what drives our trust in them, and the many ramifications of algorithmic decision-making. He examines episodes like Microsoft's chatbot Tay, which was designed to converse on social media like a teenage girl, but instead turned sexist and racist; the fatal accidents of self-driving cars; and even our own common, and often frustrating, experiences on services like Netflix and Amazon. A Human's Guide to Machine Intelligence is an entertaining and provocative look at one of the most important developments of our time and a practical user's guide to this first wave of practical artificial intelligence. |
| ai and risk management: Measuring and Managing Information Risk Jack Freund, Jack Jones, 2014-08-23 Using the factor analysis of information risk (FAIR) methodology developed over ten years and adopted by corporations worldwide, Measuring and Managing Information Risk provides a proven and credible framework for understanding, measuring, and analyzing information risk of any size or complexity. Intended for organizations that need to either build a risk management program from the ground up or strengthen an existing one, this book provides a unique and fresh perspective on how to do a basic quantitative risk analysis. Covering such key areas as risk theory, risk calculation, scenario modeling, and communicating risk within the organization, Measuring and Managing Information Risk helps managers make better business decisions by understanding their organizational risk. - Uses factor analysis of information risk (FAIR) as a methodology for measuring and managing risk in any organization. - Carefully balances theory with practical applicability and relevant stories of successful implementation. - Includes examples from a wide variety of businesses and situations presented in an accessible writing style. |
| ai and risk management: 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 and risk management: An Introduction to Ethics in Robotics and AI Christoph Bartneck, Christoph Lütge, Alan Wagner, Sean Welsh, 2020-08-11 This open access book introduces the reader to the foundations of AI and ethics. It discusses issues of trust, responsibility, liability, privacy and risk. It focuses on the interaction between people and the AI systems and Robotics they use. Designed to be accessible for a broad audience, reading this book does not require prerequisite technical, legal or philosophical expertise. Throughout, the authors use examples to illustrate the issues at hand and conclude the book with a discussion on the application areas of AI and Robotics, in particular autonomous vehicles, automatic weapon systems and biased algorithms. A list of questions and further readings is also included for students willing to explore the topic further. |
| ai and risk management: The AI Book Ivana Bartoletti, Anne Leslie, Shân M. Millie, 2020-06-29 Written by prominent thought leaders in the global fintech space, The AI Book aggregates diverse expertise into a single, informative volume and explains what artifical intelligence really means and how it can be used across financial services today. Key industry developments are explained in detail, and critical insights from cutting-edge practitioners offer first-hand information and lessons learned. Coverage includes: · Understanding the AI Portfolio: from machine learning to chatbots, to natural language processing (NLP); a deep dive into the Machine Intelligence Landscape; essentials on core technologies, rethinking enterprise, rethinking industries, rethinking humans; quantum computing and next-generation AI · AI experimentation and embedded usage, and the change in business model, value proposition, organisation, customer and co-worker experiences in today’s Financial Services Industry · The future state of financial services and capital markets – what’s next for the real-world implementation of AITech? · The innovating customer – users are not waiting for the financial services industry to work out how AI can re-shape their sector, profitability and competitiveness · Boardroom issues created and magnified by AI trends, including conduct, regulation & oversight in an algo-driven world, cybersecurity, diversity & inclusion, data privacy, the ‘unbundled corporation’ & the future of work, social responsibility, sustainability, and the new leadership imperatives · Ethical considerations of deploying Al solutions and why explainable Al is so important |
| ai and risk management: Revisiting Supply Chain Risk George A. Zsidisin, Michael Henke, 2018-12-18 This book offers a bridge between our current understanding of supply chain risk in practice and theory, and the monumental shifts caused by the emergence of the fourth industrial revolution. Supply chain risk and its management have experienced significant attention in scholarship and practice over the past twenty years. Our understanding of supply chain risk and its many facets, such as uncertainty and vulnerability, has expanded beyond utilizing approaches such as deploying inventory to buffer the initial effects of disruptions. Even with our increased knowledge of supply chain risk, being in the era of lean supply chain practices, digitally managed global supply chains, and closely interconnected networks, firms are exposed as ever to supply chain uncertainties that can damage, or even destroy, their ability to compete in the marketplace. The book acknowledges the criticality of big data analytics in Supply Chain Risk Management (SCRM) processes and provides appropriate tools and approaches for creating robust SCRM processes. Revisiting Supply Chain Risk presents a state-of-the-art look at SCRM through current research and philosophical thought. It is divided into six sections that highlight established themes, as well as provide new insights to developing areas of inquiry and contexts on the topic. Section 1 examines the first step in managing supply chain risk, risk assessment. The chapters in Section 2 encompass resiliency in supply chains, while Section 3 looks at relational and behavioral perspectives from varying units of analysis including consortiums, teams and decision makers. Section 4 focuses on examining supply chain risk in the contexts of sustainability and innovation. Section 5 provides insight on emerging typologies and taxonomies for classifying supply chain risk. The book concludes with Section 6, featuring illustrative case studies as real-world examples in assessing and managing supply chain risk. |
| ai and risk management: Artificial Intelligence in Asset Management Söhnke M. Bartram, Jürgen Branke, Mehrshad Motahari, 2020-08-28 Artificial intelligence (AI) has grown in presence in asset management and has revolutionized the sector in many ways. It has improved portfolio management, trading, and risk management practices by increasing efficiency, accuracy, and compliance. In particular, AI techniques help construct portfolios based on more accurate risk and return forecasts and more complex constraints. Trading algorithms use AI to devise novel trading signals and execute trades with lower transaction costs. AI also improves risk modeling and forecasting by generating insights from new data sources. Finally, robo-advisors owe a large part of their success to AI techniques. Yet the use of AI can also create new risks and challenges, such as those resulting from model opacity, complexity, and reliance on data integrity. |
| ai and risk management: OECD Business and Finance Outlook 2021 AI in Business and Finance OECD, 2021-09-24 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. |
| ai and risk management: 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 |
| ai and risk management: 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. |
| ai and risk management: Systemic Banking Crises Revisited Mr.Luc Laeven, Mr.Fabian Valencia, 2018-09-14 This paper updates the database on systemic banking crises presented in Laeven and Valencia (2008, 2013). Drawing on 151 systemic banking crises episodes around the globe during 1970-2017, the database includes information on crisis dates, policy responses to resolve banking crises, and the fiscal and output costs of crises. We provide new evidence that crises in high-income countries tend to last longer and be associated with higher output losses, lower fiscal costs, and more extensive use of bank guarantees and expansionary macro policies than crises in low- and middle-income countries. We complement the banking crises dates with sovereign debt and currency crises dates to find that sovereign debt and currency crises tend to coincide or follow banking crises. |
| ai and risk management: Knowledge-Based Risk Management in Engineering Kiyoshi Niwa, 1989-02-13 Based on the author's ten years of research, development, and implementation of knowledge-based systems for project risk management, this book addresses the application of artificial intelligence (AI) to management, and attempts to attain a new level of AI and risk management for large construction projects. It presents a new concept--a human-computer cooperative system--as the next generation knowledge-based system for application to ill-structured management fields. The human-computer cooperative system incorporates human intuitive abilities into the computer system. A case study is used involving a risk management case for large construction projects with the resulting discussions and methods applicable to other management fields as well. |
| ai and risk management: The Risk-Wise Investor Michael T. Carpenter, 2009-08-13 User-friendly risk management tools, tips, and techniques for a less certain world Though a very high level of investor uncertainty, anxiety, and concern about risk now exists, the vast majority of investors do not genuinely understand investment risk-let alone how to effectively manage it. The Risk-Wise Investor offers a totally new, user-friendly, non-technical way to help you better understand and manage uncertainty and risk. This practical guide will help investors avoid many common pitfalls and make well informed, knowledge-based decisions when facing uncertainty and risk. It also shows how to implement a personalized, systematic risk management planning process that will allow you to manage the risks you face more effectively and improve the likelihood of achieving specific investment goals. Though traditional investment advice is based on taking the long view and diversifying portfolios, the information here shows how to incorporate additional risk management considerations into your plans. The Risk-Wise Investor also provides innovative insights that will help investors and their advisors better understand how to: Gain a practical, user-friendly, knowledge based understanding of risk and risk management Better understand and manage financial uncertainty and rapid change Release life-risk management skills in the world of investments Become less anxious, more knowledgeable, realistic, and potentially more successful investors Learn a new empowering definition of risk to more effectively address risk and uncertainty Help reduce the likelihood and potential impact of negative surprises |
| ai and risk management: HBR Guide to Making Better Decisions Harvard Business Review, 2020-02-11 Learn how to make better; faster decisions. You make decisions every day--from prioritizing your to-do list to choosing which long-term innovation projects to pursue. But most decisions don't have a clear-cut answer, and assessing the alternatives and the risks involved can be overwhelming. You need a smarter approach to making the best choice possible. The HBR Guide to Making Better Decisions provides practical tips and advice to help you generate more-creative ideas, evaluate your alternatives fairly, and make the final call with confidence. You'll learn how to: Overcome the cognitive biases that can skew your thinking Look at problems in new ways Manage the trade-offs between options Balance data with your own judgment React appropriately when you've made a bad choice Communicate your decision--and overcome any resistance Arm yourself with the advice you need to succeed on the job, from a source you trust. Packed with how-to essentials from leading experts, the HBR Guides provide smart answers to your most pressing work challenges. |
| ai and risk management: Robot-Proof, revised and updated edition Joseph E. Aoun, 2024-10-15 A fresh look at a “robot-proof” education in the new age of generative AI. In 2017, Robot-Proof, the first edition, foresaw the advent of the AI economy and called for a new model of higher education designed to help human beings flourish alongside smart machines. That economy has arrived. Creative tasks that, seven years ago, seemed resistant to automation can now be performed with a simple prompt. As a result, we must now learn not only to be conversant with these technologies, but also to comprehend and deploy their outputs. In this revised and updated edition, Joseph Aoun rethinks the university’s mission for a world transformed by AI, advocating for the lifelong endeavor of a “robot-proof” education. Aoun puts forth a framework for a new curriculum, humanics, which integrates technological, data, and human literacies in an experiential setting, and he renews the call for universities to embrace lifelong learning through a social compact with government, employers, and learners themselves. Drawing on the latest developments and debates around generative AI, Robot-Proof is a blueprint for the university as a force for human reinvention in an era of technological change—an era in which we must constantly renegotiate the shifting boundaries between artificial intelligence and the capacities that remain uniquely human. |
| ai and risk management: Enterprise Security Risk Management Brian Allen, Esq., CISSP, CISM, CPP, CFE, Rachelle Loyear CISM, MBCP, 2017-11-29 As a security professional, have you found that you and others in your company do not always define “security” the same way? Perhaps security interests and business interests have become misaligned. Brian Allen and Rachelle Loyear offer a new approach: Enterprise Security Risk Management (ESRM). By viewing security through a risk management lens, ESRM can help make you and your security program successful. In their long-awaited book, based on years of practical experience and research, Brian Allen and Rachelle Loyear show you step-by-step how Enterprise Security Risk Management (ESRM) applies fundamental risk principles to manage all security risks. Whether the risks are informational, cyber, physical security, asset management, or business continuity, all are included in the holistic, all-encompassing ESRM approach which will move you from task-based to risk-based security. How is ESRM familiar? As a security professional, you may already practice some of the components of ESRM. Many of the concepts – such as risk identification, risk transfer and acceptance, crisis management, and incident response – will be well known to you. How is ESRM new? While many of the principles are familiar, the authors have identified few organizations that apply them in the comprehensive, holistic way that ESRM represents – and even fewer that communicate these principles effectively to key decision-makers. How is ESRM practical? ESRM offers you a straightforward, realistic, actionable approach to deal effectively with all the distinct types of security risks facing you as a security practitioner. ESRM is performed in a life cycle of risk management including: Asset assessment and prioritization. Risk assessment and prioritization. Risk treatment (mitigation). Continuous improvement. Throughout Enterprise Security Risk Management: Concepts and Applications, the authors give you the tools and materials that will help you advance you in the security field, no matter if you are a student, a newcomer, or a seasoned professional. Included are realistic case studies, questions to help you assess your own security program, thought-provoking discussion questions, useful figures and tables, and references for your further reading. By redefining how everyone thinks about the role of security in the enterprise, your security organization can focus on working in partnership with business leaders and other key stakeholders to identify and mitigate security risks. As you begin to use ESRM, following the instructions in this book, you will experience greater personal and professional satisfaction as a security professional – and you’ll become a recognized and trusted partner in the business-critical effort of protecting your enterprise and all its assets. |
| ai and risk management: Enterprise Risk Management John R. S. Fraser, Betty Simkins, 2010-01-07 Essential insights on the various aspects of enterprise risk management If you want to understand enterprise risk management from some of the leading academics and practitioners of this exciting new methodology, Enterprise Risk Management is the book for you. Through in-depth insights into what practitioners of this evolving business practice are actually doing as well as anticipating what needs to be taught on the topic, John Fraser and Betty Simkins have sought out the leading experts in this field to clearly explain what enterprise risk management is and how you can teach, learn, and implement these leading practices within the context of your business activities. In this book, the authors take a broad view of ERM, or what is called a holistic approach to ERM. Enterprise Risk Management introduces you to the wide range of concepts and techniques for managing risk in a holistic way that correctly identifies risks and prioritizes the appropriate responses. This invaluable guide offers a broad overview of the different types of techniques: the role of the board, risk tolerances, risk profiles, risk workshops, and allocation of resources, while focusing on the principles that determine business success. This comprehensive resource also provides a thorough introduction to enterprise risk management as it relates to credit, market, and operational risk, as well as the evolving requirements of the rating agencies and their importance to the overall risk management in a corporate setting. Filled with helpful tables and charts, Enterprise Risk Management offers a wealth of knowledge on the drivers, the techniques, the benefits, as well as the pitfalls to avoid, in successfully implementing enterprise risk management. Discusses the history of risk management and more recently developed enterprise risk management practices and how you can prudently implement these techniques within the context of your underlying business activities Provides coverage of topics such as the role of the chief risk officer, the use of anonymous voting technology, and risk indicators and their role in risk management Explores the culture and practices of enterprise risk management without getting bogged down by the mathematics surrounding the more conventional approaches to financial risk management This informative guide will help you unlock the incredible potential of enterprise risk management, which has been described as a proxy for good management. |
| ai and risk management: AI in Insurance: Revolutionizing Risk Management Dizzy Davidson, 2024-08-29 Are you struggling to fully grasp the potential of AI in the insurance industry? Wondering how AI can transform risk management and streamline operations? Look no further! “AI in Insurance: Revolutionizing Risk Management” is your comprehensive guide to understanding and leveraging AI in the insurance sector. This book demystifies AI, offering clear insights and practical applications that can transform your business. Benefits of Reading This Book: Enhanced Efficiency: Learn how AI can automate claims processing, reducing time and errors. Fraud Prevention: Discover AI techniques to detect and prevent fraudulent activities. Accurate Risk Assessment: Understand how AI can improve underwriting and risk management. Customer Satisfaction: Explore AI-driven customer service solutions that enhance user experience. Personalized Products: Find out how AI can tailor insurance products to individual needs. This book is packed with real-world examples and case studies, making complex concepts easy to understand. Whether you’re an insurance professional, a tech enthusiast, or someone curious about AI, this book provides valuable insights and actionable strategies. Why This Book is Essential: Comprehensive Coverage and Covers all aspects of AI in insurance, from claims processing to marketing. Practical Applications and Offers actionable strategies that can be implemented in your business. Expert Insights and Written by industry experts with deep knowledge of AI and insurance. Future-Proofing and Prepares you for the future of insurance with AI-driven innovations. Key Topics Covered: Automating Claims Processing Fraud Detection and Prevention Underwriting and Risk Assessment AI-Powered Customer Service Personalized Insurance Products Predictive Analytics Telematics and Usage-Based Insurance Document Processing Actuarial Analysis Marketing and Sales Strategies Don’t miss out on the opportunity to revolutionize your insurance business with AI. Get your copy of “AI in Insurance: Revolutionizing Risk Management” today and unlock the full potential of AI in the insurance industry. Become knowledgeable about AI and stay ahead in the competitive market! |
| ai and risk management: The Essentials of Risk Management, Second Edition Michel Crouhy, Dan Galai, Robert Mark, 2013-12-06 The essential guide to quantifying risk vs. return has been updated to reveal the newest, most effective innovations in financial risk management Written for risk professionals and non-risk professionals alike, this easy-to-understand guide helps readers meet the increasingly insistent demand to make sophisticated assessments of their company’s risk exposure Provides the latest methods for measuring and transferring credit risk, increase risk-management transparency, and implement an organization-wide Enterprise risk Management (ERM) approach The authors are renowned figures in risk management: Crouhy heads research and development at NATIXIS; Galai is the Abe Gray Professor of Finance and Business Asdministration at Hebrew University; and Mark is the founding CEO of Black Diamond Risk |
| ai and risk management: The Risk IT Practitioner Guide Isaca, 2009 |
| ai and risk management: The Standard for Risk Management in Portfolios, Programs, and Projects Project Management Institute, 2019-04-22 This is an update and expansion upon PMI's popular reference, The Practice Standard for Project Risk Management. Risk Management addresses the fact that certain events or conditions may occur with impacts on project, program, and portfolio objectives. This standard will: identify the core principles for risk management; describe the fundamentals of risk management and the environment within which it is carried out; define the risk management life cycle; and apply risk management principles to the portfolio, program, and project domains within the context of an enterprise risk management approach It is primarily written for portfolio, program, and project managers, but is a useful tool for leaders and business consumers of risk management, and other stakeholders. |
| ai and risk management: Agile Practice Guide , 2017-09-06 Agile Practice Guide – First Edition has been developed as a resource to understand, evaluate, and use agile and hybrid agile approaches. This practice guide provides guidance on when, where, and how to apply agile approaches and provides practical tools for practitioners and organizations wanting to increase agility. This practice guide is aligned with other PMI standards, including A Guide to the Project Management Body of Knowledge (PMBOK® Guide) – Sixth Edition, and was developed as the result of collaboration between the Project Management Institute and the Agile Alliance. |
| ai and risk management: Emerging Trends and Applications in Cognitive Computing Mallick, Pradeep Kumar, Borah, Samarjeet, 2018-12-28 Though an individual can process a limitless amount of information, the human brain can only comprehend a small amount of data at a time. Using technology can improve the process and comprehension of information, but the technology must learn to behave more like a human brain to employ concepts like memory, learning, visualization ability, and decision making. Emerging Trends and Applications in Cognitive Computing is a fundamental scholarly source that provides empirical studies and theoretical analysis to show how learning methods can solve important application problems throughout various industries and explain how machine learning research is conducted. Including innovative research on topics such as deep neural networks, cyber-physical systems, and pattern recognition, this collection of research will benefit individuals such as IT professionals, academicians, students, researchers, and managers. |
| ai and risk management: The Turing Test Stuart M. Shieber, 2004-06-18 Historical and contemporary papers on the philosophical issues raised by the Turing Test as a criterion for intelligence. The Turing Test is part of the vocabulary of popular culture—it has appeared in works ranging from the Broadway play Breaking the Code to the comic strip Robotman. The writings collected by Stuart Shieber for this book examine the profound philosophical issues surrounding the Turing Test as a criterion for intelligence. Alan Turing's idea, originally expressed in a 1950 paper titled Computing Machinery and Intelligence and published in the journal Mind, proposed an indistinguishability test that compared artifact and person. Following Descartes's dictum that it is the ability to speak that distinguishes human from beast, Turing proposed to test whether machine and person were indistinguishable in regard to verbal ability. He was not, as is often assumed, answering the question Can machines think? but proposing a more concrete way to ask it. Turing's proposed thought experiment encapsulates the issues that the writings in The Turing Test define and discuss. The first section of the book contains writings by philosophical precursors, including Descartes, who first proposed the idea of indistinguishablity tests. The second section contains all of Turing's writings on the Turing Test, including not only the Mind paper but also less familiar ephemeral material. The final section opens with responses to Turing's paper published in Mind soon after it first appeared. The bulk of this section, however, consists of papers from a broad spectrum of scholars in the field that directly address the issue of the Turing Test as a test for intelligence. Contributors John R. Searle, Ned Block, Daniel C. Dennett, and Noam Chomsky (in a previously unpublished paper). Each chapter is introduced by background material that can also be read as a self-contained essay on the Turing Test |
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