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Paperback. Etat : new. Paperback. The detection era is over. The redesign era has begun. Generative AI didn't create a cheating crisis in higher education. It created an evidence crisis. When any student can produce a polished essay, case analysis, or research report in seconds, the artifacts institutions have long treated as proof of learning no longer prove anything at all. The inferential link between what students produce and what students know has been severed. Not by dishonest students, but by a technology that made producing convincing academic work effortless. Most institutions responded the way institutions always respond: they bought tools. AI detection software promised to sort human writing from machine writing and restore the old order. It hasn't worked. Not because the tools aren't clever enough, but because adversarial dynamics guarantee that evasion will always be cheaper, faster, and more accessible than detection. Every improvement in detection triggers a trivial workaround. The arms race is structurally unwinnable, and the institutions investing in it are spending resources and political capital on a strategy that delays the work that actually matters. Meanwhile, the real problem goes unaddressed. Assessment systems across higher education were built for cooperative conditions-environments where producing a convincing academic artifact genuinely required the cognitive work that artifact was supposed to represent. That assumption held for decades. It no longer holds. And no amount of detection, no revised rubric, and no AI-acceptable-use policy will make it hold again. Assessment Under Adversarial Pressure argues that the only durable institutional response is architectural. Not better assignments. Not updated honor codes. Not plagiarism software with a new label. The response is redesigning how evidence of learning is generated, collected, and certified-so that the evidence holds up regardless of what tools students have access to. The book introduces adversarial assessment as a design framework: an approach that treats AI-enabled delegation not as a violation to be policed, but as a permanent condition to be designed around. Drawing on validity theory from educational measurement, adversarial design principles from security engineering, and the most current research available, it provides institutional leaders with a framework for rethinking assessment architecture from the ground up. What this book covers: - Why detection-based strategies are structurally unwinnable-and why institutions keep pursuing them anyway- Why the most common assignment redesigns fail under even moderate adversarial pressure- How adversarial assessment reframes the problem from student behavior to system design- How to build evidence systems whose trustworthiness comes from conditions, not compliance- What accreditation bodies are starting to expect-and what their standards still assume- How to navigate the legal exposure that detection-dependent policies create- How to sequence institutional change when consensus is slow and the environment moves fast- Why the real problem was never about AI-it was about evidentiary assumptions that were always fragile Written for: Provosts, deans, assessment directors, accreditation liaisons, institutional effectiveness officers, program directors, faculty senate leaders, department chairs, and graduate students in higher education programs. If your role involves certifying that students have learned what your institution claims they have learned, this book is for you. This is not a teaching guide. It is not a technology review. It is not a defense of AI or an attack on students. It is an argument about institutional architecture-and a practical framework for leaders who canno Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Etat : New.

Etat : As New. Unread book in perfect condition.

Etat : As New. Unread book in perfect condition.

Etat : As New. Unread book in perfect condition.

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paperback. Etat : Very Good. Ships Out Tomorrow.

Etat : New. In English.

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Hardcover. Etat : Wie neu. 348 S.; Ill. Like new. Shrink wrapped. Sprache: Englisch Gewicht in Gramm: 1000.

Etat : Very Good. 1st Edition. Former library copy. Pages intact with possible writing/highlighting. Binding strong with minor wear. Dust jackets/supplements may not be included. Includes library markings. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.…

Etat : New. In English.

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Etat : As New. Unread book in perfect condition.

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Etat : As New. Unread book in perfect condition.

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Etat : As New. Unread book in perfect condition.

Etat : As New. Unread book in perfect condition.

Etat : As New. Unread book in perfect condition.

Etat : New.

Etat : New.

Etat : As New. Unread book in perfect condition.

1st ed. 18 x 25 cm. 312 pages. HC Versand aus Deutschland / We dispatch from Germany via Air Mail. Einband bestoßen, daher Mängelexemplar gestempelt, sonst sehr guter Zustand. Imperfect copy due to slightly bumped cover, apart from this in very good condition. Stamped. Sprache: Englisch.

Etat : New.

Paperback. Etat : New. As artificial intelligence becomes embedded in everything from healthcare diagnostics to financial systems and autonomous vehicles, the stakes for AI security have never been higher. Adversarial AI Threat Response and Secure Model Design is your essential guide to understanding, defending against, and designing resilient machine learning systems in the face of growing adversarial threats.Written by a leading expert in AI security and policy, this book delivers a combination of technical depth, practical implementation, and strategic insight. It begins by mapping the full landscape of adversarial threats-evasion, poisoning, model extraction, backdoors, and more-across diverse data modalities and real-world applications. From there, it equips readers with a robust toolkit of detection and defense techniques, including adversarial training, anomaly detection, and formal robustness certification.But this book goes beyond code. It explores the organizational, ethical, and regulatory dimensions of AI security, offering guidance on risk quantification, explainability, and compliance with frameworks like the EU AI Act. With hands-on projects, open-source tools, and case studies in high-stakes domains, readers will learn to design secure-by-default systems that are not only technically sound but socially responsible.Whether you're an AI engineer deploying models in production, a cybersecurity professional defending intelligent systems, or an educator preparing the next generation of AI talent, this book provides the clarity, rigor, and foresight needed to stay ahead of adversarial threats. It's not just a reference-it's a roadmap for building trustworthy AI.  What You Will Learn:Understand the full spectrum of adversarial threats to AI systems, including evasion, poisoning, backdoor injection, and model extraction, across vision, language, and multimodal applications.Apply practical detection and defense techniques using real tools and code, including adversarial training, statistical anomaly detection, input preprocessing, and ensemble defenses.Evaluate and balance trade-offs between accuracy, robustness, performance, and interpretability in the design of secure machine learning systems.Navigate the regulatory, ethical, and risk management challenges associated with adversarial AI, including disclosure practices, auditability, and compliance with emerging AI laws.Design, implement, and test secure-by-design AI solutions through hands-on projects and real-world case studies that span sectors such as healthcare, finance, and autonomous systems.Who This Book is for:Written for technical professionals and researchers who are building, deploying, or securing machine learning systems in real-world environments. The primary audience includes machine learning engineers, AI developers, cybersecurity professionals, and graduate-level students in computer science, data science, and applied AI programs. It is also relevant for technical leads, architects, and academ.…