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Martin D: Bears & Mosquitoes

Bears & Mosquitoes

Building Trusted AI from the Inside Out - A Field Guide to AI That Earns Its Keep

Computers, Privacy and Data Protection

Who decides how data and AI are governed, and whose rights prevail?This open access book brings together contributions from the CPDP conference in Brussels, Europe's leading forum on privacy and data protection. Ten years after the GDPR, digital governance stands at a crossroads. The EU has layered the DSA, DMA, Data Act and AI Act onto its data protection regime, while calls for simplification grow louder and governments from Brazil and Mexico to Canada and Southeast Asia pursue their own regulatory visions. The book examines algorithmic oversight and automated decision-making in the UK, AI and directors' duties in Canadian corporate law, data governance for connected vehicles in the EU, GDPR-inspired access rights across Japan and Southeast Asia, the Brussels Effect in Brazil and Mexico, location-data provenance and digital trust infrastructures, and the inequalities produced by biometric and health data infrastructures in Europe and India.The chapters show that governance failures rarely stem from a shortage of rules. They arise when regulatory visions, technical architectures and institutions pull in different directions. Read together, they make the case for treating disagreement not as an obstacle but as a democratic resource.With its blend of doctrinal analysis, critical theory and socio-technical insight, this book is for academics, policymakers, practitioners, DPOs and students working on data protection, AI regulation and digital society.The ebook editions of this book are available open access under a CC BY-NC-ND 4.0 licence on bloomsburycollections.com.

Competing Visions, Shared Futures

Data Protection, Privacy and Artificial Intelligence, Volume 19

This textbook examines how artificial intelligence, machine learning, generative AI, and data-driven decision systems are reshaping financial services. Moving from core AI concepts to applied financial use cases, it covers credit risk assessment, fraud detection, Monte Carlo methods, stock and cryptocurrency valuation, knowledge graphs, AI-enabled IoT, large language models, Retrieval-Augmented Generation (RAG), AI agents, and no-code AI platforms. It also looks at infrastructure choices for enterprise AI, including cloud and on-premise deployment, before turning to sustainable finance, greenwashing detection, and AI governance in regulated environments. Throughout, the book emphasizes decision quality, transparency, accountability, model risk management, and practical implementation. It will be of interest to upper-level undergraduate and postgraduate students in finance, banking, FinTech, risk management, and business analytics, as well as practitioners seeking a structured introduction to AI applications and governance in finance.
 Migdat Hodžić
 is Principal and Chief Technology Officer at ARTI Analytics and a professor at the International University of Sarajevo and Džemal Bijedić University of Mostar. He holds a PhD from Santa Clara University and works across artificial intelligence, machine learning, optimization, and quantitative finance. He has led applied AI research and product development in Europe and the United States and is an inventor on multiple US patents.
 Tarik Hubana
 is Chief Operating Officer at ARTI Analytics and Assistant Professor at Džemal Bijedić University of Mostar. He holds a PhD in technical sciences from TU Graz and works on applied AI, machine learning, predictive modelling, and enterprise AI systems. His academic and industry experience spans analytics, digital transformation, and AI deployment in data-intensive sectors. He leads applied AI research and product development in Europe and the United States and is an inventor on multiple US patents.
 Admir Mešković
 is Assistant Professor of Finance at the International University of Sarajevo and Manager of the Center for Islamic Finance, Innovation and Sustainability. He holds a PhD in Finance from the University of Sarajevo and an MBA from Durham University Business School. His research focuses on Islamic finance, FinTech, risk management, and sustainable finance.

From Theory to Practice

Artificial Intelligence in Finance

Build an AI security operating model that manages risk, strengthens governance and enables AI to scale securely. AI Risk Management is a practical guide for AI risk owners, GRC managers, AI governance managers and technology risk professionals responsible for managing the security risks of AI.Written by security expert Rafah Knight, this book shows how to make AI security operational, measurable and embedded into existing business processes. It provides practical guidance on establishing ownership and accountability for AI risk, designing effective governance structures, implementing policies and controls and managing third-party AI vendor exposure.Drawing on real-world examples from financial services, retail, the public sector and healthcare, learn how to: - Build an effective AI security operating model - Establish clear ownership and accountability for AI risk - Design AI governance policies, controls and assurance processes - Measure AI security and operational risks effectively - Manage third-party AI vendor exposure at scaleAI Security gives you the practical structures and controls to operationalize AI assurance, strengthen AI governance and support secure AI adoption at scale.Themes include: AI security, AI risk management, AI governance, GRC, AI assurance, third-party AI risk

How to Scale AI Securely

AI Risk Management

Turn AI investment into scalable business impact by enabling your workforce to build the solutions they actually need. From AI Users to Solution Builders is a strategic guide for senior AI, technology, and transformation leaders who want to move beyond underperforming, centralized AI initiatives, and unlock real enterprise value. As organizations struggle to translate AI into measurable results, this book shows how shifting from top-down deployment to workforce-led solution building creates faster adoption, stronger ROI, and sustained innovation. Written by experienced AI leader Wei Manfredi, this book combines strategic insight with practical execution. It draws clear parallels between failed digital transformation efforts and current AI adoption challenges, while demonstrating how natural language interfaces are reshaping access to AI. By enabling non-technical professionals to design their own tools, organizations can embed AI directly into workflows, decision-making, and daily operations. You'll learn how to: - Shift from centralized AI models to workforce-driven innovation - Enable employees to build AI solutions for real workflows - Design scalable infrastructure for AI solution development - Apply change management to overcome resistance and drive adoption &gt;Featuring real-world examples from organizations including Lululemon, Google, Oracle, and JPMorgan Chase, this book provides a proven framework for embedding AI into everyday collaboration, accelerating innovation, and future-proofing your organization's competitive advantage. Themes include: AI strategy, AI adoption, workforce transformation, AI governance, enterprise AI, change management

Scaling AI Across Your Organization

From AI Users to Solution Builders

Language Operations: The Operating System for Global Communication - A Workbook for Executives

The LangOps Playbook

Baustein einer Wirtschafts- und Gesellschaftstransformation

Gemeinwohl-Ökonomie (GWÖ)

Turn enterprise AI into a source of strategic insight, not just productivity gains.The AI Gap by leading AI expert and keynote speaker Richard Foster-Fletcher shows business and technology leaders how to unlock greater value from enterprise AI by changing the structures, processes and culture surrounding it. Organizations invest in AI expecting new intelligence and transformative insights. Yet AI is often introduced through procurement, security, legal and compliance systems designed for standard software, restricting its potential before it can deliver on that promise. This book explains why this gap persists and what organizations must change to close it. You'll learn how to: - Identify organizational barriers limiting the value of AI - Move beyond productivity-focused AI implementation - Create conditions where AI can generate deeper insights - Enable employees to explore, experiment and challenge established thinking &gt;The AI Gap equips leaders to rethink AI implementation, create space for experimentation and turn artificial intelligence into a powerful driver of organizational insight and long-term business value. Themes include: enterprise AI, AI implementation, AI strategy, business transformation, organizational culture, AI innovation

How Business Decisions Expand What AI Can Do

The AI Gap

Getting a large language model to load on your own hardware takes an afternoon. Keeping it serving real traffic, at a cost you can predict, is a different job, and it is the one this book covers. It does so by building one system and growing it across all fifteen chapters.
You begin with an 8B model on a single card, the kind you may already own or can rent for well under a dollar an hour. From there the book adds what real traffic needs: a choice of inference engine, a control plane that provisions GPUs and tears them down again, a router and queue that decide what runs next, and cache-aware routing that stops a conversation being re-read from scratch on every turn. Part III moves to models that do not fit on one card, placing a 70B deployment across GPUs chosen for the bandwidth between them as much as for their memory, and buying that capacity from several providers at once.
In the final part you serve a 753-billion-parameter sparse model on eight rented H200s, measure what each session costs as concurrency rises, and find the point where a larger batch stops making sessions cheaper. That result explains how commercial inference providers price what they sell, and when self-hosting is cheaper than their APIs.
Costs come from real rented-GPU runs, and the book says which numbers are measurements and which are predictions. The control plane is open source and written in Go, so you can run every step yourself.
You Will:
<ul>
<li>Build a working inference control plane in Go that provisions GPUs across providers, deploys engines onto them, routes and queues requests, and reports what it is doing.</li>
<li>Learn how inference uses a GPU, covering KV cache growth, quantization formats and what they cost, tensor and expert parallelism, and why a fast interconnect matters more than total memory.</li>
<li>Work out per-session cost from real runs as batch size and context length grow, and find the point where self-hosting stops beating a commercial API.</li>
</ul>
This book is for:
Backend, systems and platform engineers who want to run LLMs on infrastructure they control. Also useful to infrastructure and DevOps engineers building internal AI platforms, and to technical founders with data-residency or cost constraints that rule out third-party APIs. No machine learning background is assumed. Readers should be comfortable with a systems language such as Go or Python, with containers, and with HTTP APIs.

Building and Operating Production AI Systems from a Single GPU to Multi-Node Frontier Models

Self-Hosted LLM Inference

Non-human identities now outnumber people, and someone must sign for what the machines do.
Defensible Identity Architecture is a professional reference by Professor Kai London. It covers non-human identities, agentic AI, the regulatory crosswalk, machine accountability, decision rights architecture, the identity control plane, SPIFFE and workload identity, ITDR and cyber-insurance posture.
Written for boards, executives, CISOs, engineers, risk and compliance leaders and practitioners who need clear, defensible guidance they can put to work, the book combines explanation with practical frameworks, checklists and decision aids.
Key topics include: non-human identities, agentic AI, the regulatory crosswalk, machine accountability, decision rights architecture, the identity control plane, SPIFFE, workload identity, ITDR, cyber-insurance posture.

Defensible Identity Architecture

Build a resilient, high-performing organization by integrating agentic AI into strategy, operations, and decision-making. Building an Agentic Organization is a strategic guide for senior technology, AI, and business leaders responsible for delivering value from agentic AI while managing its risks. As organizations move toward human and AI collaboration at scale, leaders must balance innovation with governance, adoption, and long-term economic impact.Written by experienced AI executive Geoffrey Schaefer, this book provides a comprehensive framework for designing an agentic organization by examining strategy, governance, and economics. It explores how to define the purpose of agentic AI, align it with business objectives, and create the structures needed to support safe, scalable adoption. Addressing both opportunity and risk, it equips leaders to navigate workforce concerns, reputational exposure, and the evolving role of AI agents in enterprise environments.You'll learn how to: - Define a clear strategy for integrating agentic AI - Establish governance frameworks to manage risk and accountability - Drive workforce adoption and address resistance to AI collaboration - Assess economic value and impact of agentic AI systems - Manage reputational and operational risks from AI agent behaviorFeaturing real-world examples from Leidos, JPMorgan Chase, SY Partners, and OMA, this book provides a practical roadmap for embedding agentic AI into the fabric of your organization while protecting value, trust, and long-term performance.Themes include: agentic AI, AI strategy, AI governance, AI transformation, enterprise AI, risk management

The Strategy, Governance, and Economics of Agentic AI

Building an Agentic Organization

This executive book is a standalone work, not a second edition of Agentic by Design. It is intentionally vendor-neutral and references product names only as examples of categories. It includes no code, configuration, or implementation plan. This book is intended for decision-makers, not implementers.

How AI Governance Became the Growth Engine

Governed Autonomy

Implement trusted AI systems that deliver measurable business value while managing risk, compliance and long-term performance. Decision-Quality AI by Eva-Marie Muller-Stuler is a practical guide for data, product and technology leaders responsible for turning AI ambition into real-world outcomes. Covering the complete AI lifecycle from strategy and team design to deployment and governance, it provides the frameworks and technical foundations needed to develop AI products that are effective, responsible and aligned with business goals. Designed for leaders implementing AI at scale, this book helps you move beyond experimentation and hype to build AI solutions that solve genuine business challenges while meeting regulatory and operational requirements. You will learn how to: - Define AI use cases that address real business needs - Build teams, processes and governance for AI delivery - Develop, test and deploy AI with confidence - Manage risk, compliance, security and transparency - Monitor performance throughout the AI lifecycle Decision-Quality AI combines responsible AI principles with the practical disciplines required for successful implementation. It explores areas such as MLOps, ModelOps, AIOps, DevSecOps and cybersecurity, giving you a complete framework for delivering AI that creates lasting impact. Whether you are launching your first AI initiative or scaling enterprise-wide adoption, this book provides the tools, processes, and leadership guidance you need to build trusted AI systems that deliver results. Themes include: AI development, AI governance, AI security, MLOps, ModelOps, risk management, DevSecOps, data architecture, AI strategy

From AI Ambition to Operational Success

Decision-Quality AI

Bears & Mosquitoes

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