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AI's Transformative Impact on Finance,
Auditing, and Investment

AI's Transformative Impact on Finance, Auditing, and Investment

The Essential Guide to Launching Your Career in Artificial Intelligence

Break into AI

Theory and Lessons from Leading Companies

AI-Powered Functions

Artificial Intelligence-Driven Digital Transformation

International Series in Operations Research & Management Science

This book presents a rigorous, practitioner-ready framework for determining when interventional causal claims can be validly drawn from observational data. By integrating potential outcomes (PO), structural causal models (SCMs), directed acyclic graphs (DAGs), quasi-experimental and longitudinal designs, information-theoretic approaches, and modern machine-learning methods with decision-analytic principles, it identifies necessary conditions for each step of the causal inference process, from defining estimands and identifying causal effects to study design, measurement, estimation, interpretation, robustness, and external validity. The framework is operationalized through practical checklists, tables, diagnostic and falsification tests, and AI-assisted tools, making it accessible across fields such as epidemiology, environmental and occupational health, and regulatory risk analysis. Designed to meet the needs of professionals making high-stakes decisions based on nonexperimental data, the book addresses the lack of clear, testable criteria for credible causal inference. Real-world examples throughout the book illustrate diagnostics, falsification tests, and robustness checks, linking evidence directly to decision-making. Bridging multiple causal frameworks and modern machine-learning methods, it equips readers to assess when data support cause-and-effect conclusions, supporting more responsible, evidence-based policy and risk management.The book offers health risk analysts, epidemiologists, and policy professionals a practical guide to valid causal inference from observational data. It is also ideal for graduate students and workshop participants in data science, decision analysis, risk analysis, and AI/ML applications.

Valid Causal Inference from Observational Data

AI Forward Future: How Companies Can Grow, Defend, and Innovate with Artificial Intelligence (AI) by Jeremy A. Swenson is more than just another book on AI-it's a dependable roadmap for those eager to harness AI's transformative power while managing its inherent risks to achieve market leadership. The book is for the real power players-business leaders, policy makers, regulators, technology professionals, risk managers, digital strategists, and entrepreneurs-who refuse to accept superficial AI hype. It offers strong, actionable strategies to grow, defend, and innovate with AI, covering machine learning (ML), natural language processing (NLP), Agentic AI, generative AI (GAI), and the hypothetical artificial superintelligence (ASI). Through riveting case studies across various industries and eras, the book connects the dots, revealing the trade-offs, risks, secrets, synergies, and solutions behind modern innovation. The real question is, will you master AI Forward Future, or will it master you!"A well informed entrepreneur speaking to other entrepreneurs about the promise and risks tied to AI in leading business and innovation. Reading this book is one clear step forward in securing your future."-William H. Dutton, Oxford Martin Fellow at the University of Oxford and Director of the Portulans Institute in Washington, DC (a globally recognized leader in digital research), founding Director of the Oxford Internet Institute, the first Professor of Internet Studies at Oxford, and author of The Fifth Estate: The Power Shift of the Digital Age."Jeremy Swenson's AI Forward Future offers a comprehensive exploration of the intersection between artificial intelligence and cloud security. As the Cloud Security Alliance continues to develop frameworks like the AI Controls Matrix to ensure the safe deployment of AI technologies, this book serves as a timely resource. Swenson's analysis of the challenges and opportunities presented by AI integration equips forward-thinking professionals with the knowledge to drive innovation while maintaining robust security postures."-Jim Reavis, CEO and Co-founder, Cloud Security Alliance (CSA)."Jeremy Swenson's AI Forward Future is an indispensable roadmap through the convergence of artificial intelligence and cybersecurity. With a keen understanding of these transformative technologies, the author blends technical depth with incisive analysis of the human factors-from leadership to change management-that will shape our future. This book is a must-read for anyone who seeks to harness the power of AI while navigating the complex challenges of modern cybersecurity."-David Espindola, Exponential Technologist, Futurist, former Chief Information Officer (CIO), and author of Soulful: You in the Future of Artificial Intelligence.

How Companies Can Grow, Defend, and Innovate with Artificial Intelligence (AI)

AI Forward Future

Innovations in Intelligent Internet of Everything IoE

Biomedical Applications of AI and IoMT in Smart Healthcare

Banking, Money and International Finance

Human-Centred Intelligent Finance

AI, Fintech, and the Future of Global Financial Systems

Studies in National Governance and Emerging Technologies

Opportunities and Challenges for Political Regimes

Democracy and Artificial Intelligence

How most investing decisions differ from what the data say works—and how replacing myth with measurement can improve outcomes. The Accidental Venture Capitalist examines how early-stage investment decisions are actually made—and why so many of them diverge from what the evidence says should work. Written by an academic-turned-venture capitalist Scott Shane, the book sits at the intersection of rigorous research and lived investing experience. Its central premise is simple but uncomfortable: in venture capital, folklore and intuition dominate practice, even though decades of empirical research offer more reliable guidance on selection, pricing, portfolio construction, and incentives. The book follows the full arc of the venture process, from fund design and deal sourcing to valuation, syndication, follow-on investing, governance, and exits. Each chapter takes a familiar maxim—“bet on the jockey,” “do more diligence,” “double down on winners”—and tests it against large-sample academic evidence and real investment outcomes. Shane’s analysis is grounded in power-law economics, behavioral decision-making, and incentive alignment. And he is transparent about mistakes as well as successes, treating his own funds as a living laboratory rather than a victory lap. The book also offers practical tools—checklists, heuristics, simple models—that translate evidence into decisions investors can actually implement.

Why Evidence Beats Instinct in Early-Stage Investing

The Accidental Venture Capitalist

The next choice we make is the last chance we have. Ten thousand years ago, wild cats wandered into a human grain store. Over generations, gorged with a plentiful supply of prey, they traded their raw instincts for a life of dependency as our household pets. Today, domestic cats are more comfortable than ever but they can no longer hunt effectively. Their brains are 16% smaller than their distant ancestors. They are domesticated. AI is turning the modern world into a cognitive grain store. And we are the cats. In this groundbreaking and vital book, leading AI researcher and campaigner James F. Ryan charts our relationship with transformative technologies - from writing to AI - and explains why this time it's different. Large language models like ChatGPT are not just tools, but are remaking our brains and eroding our ability to think. From the workplace and our relationships to our schools and universities, we face an urgent battle to safeguard human thought. The Domestication sets out the evidence, the imposing challenge we face, and how we respond.

A Story of AI and the Human Mind

The Domestication

SpringerBriefs in Applied Sciences and Technology

This book connects the dots between artificial intelligence, energy systems, and computing infrastructure, offering a fresh and systems-based perspective on one of the most important challenges of our time: sustainability in the age of AI. Rather than viewing AI as simply a new consumer of electricity, this book reframes it as a meta-technology —one that can drive system-wide optimization across energy, materials, logistics, and infrastructure.Using a clear systems analysis approach, this book explores how these technologies interact, how data centers are evolving into active components of energy infrastructure, and how public policy, governance, and human decision-making shape outcomes. It goes beyond the usual focus on energy efficiency or technological hype, offering readers a deeper understanding of the causal relationships and strategic implications at play.Essential reading for anyone involved in strategy, innovation, digital transformation, or sustainability, this book provides the tools to make sense of accelerating change and to act responsibly within it. It challenges assumptions, provides a practical framework for future thinking, and makes the case for deliberate, system-wide approaches to digital and ecological futures.

Why the Energy Is Well Spent

Powering the Digital Transformation

AI's Transformative Impact on Finance, Auditing, and Investment

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