Interpretable trustworthy (17 résultats)

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Paperback. Etat : Brand New. 90 pages. 6.00x0.21x9.00 inches. In Stock.

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Etat : As New. Unread book in perfect condition.

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Hardcover. Etat : Brand New. 416 pages. 9.18x6.12x9.45 inches. In Stock.

Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Artificial Intelligence is rapidly reshaping the educational landscape, transforming how institutions understand learners, predict academic outcomes, and deliver personalized learning experiences. From performance forecasting to early identification of at-risk students, AI-driven learning analytics now play a pivotal role in data-informed educational decision-making. Yet, alongside these advancements lies a critical challenge: the widespread use of opaque 'black-box' models that compromise transparency, fairness, and ethical accountability.This book offers a timely and in-depth exploration of Explainable Artificial Intelligence (XAI) and Interpretable Machine Learning (IML) as essential foundations for building trustworthy AI systems in education. It bridges the gap between technical innovation and ethical responsibility by demonstrating how techniques such as SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-agnostic Explanations) can reveal the reasoning behind model predictions. By making AI decisions understandable, educators and administrators are empowered to build trust, validate outcomes, and make informed, learner-centric decisions.Beyond methodology, the book critically examines pressing ethical concerns, including data privacy, algorithmic bias, and responsible data governance. Through real-world case studies and practical applications, it underscores the necessity of aligning AI systems with educational values, transparency standards, and robust policy frameworks. Designed for researchers, educators, and policymakers alike, this work advocates for AI in education that is not only powerful and accurate, but also explainable, ethical, and human-centered.…

Taschenbuch. Etat : Neu. Beyond the Black Box | Explainable and Ethical AI for Trustworthy Learning Analytics in Education: Interpretable ML, XAI Techniques & Responsible Decision- Making in Educational Systems | Rahul Jain | Taschenbuch | Englisch | 2026 | Eliva Press | EAN 9789999336086 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.…

Hardcover. Etat : new. Hardcover. Users expect proper explanation and interpretability of all the decisions being taken by machine and deep learning (ML/ DL) algorithms. Interpretable and Trustworthy AI: Techniques and Frameworks covers key requirements for interpretability and trustworthiness of artificial intelligence (AI) models and how these needs can be met. This book explores artificial intelligences impact, limitations, and solutions.It examines AIs role as a transformative technological paradigm. It explores how AI drives business advancement through intelligent software solutions, enabling automation, augmentation, and acceleration of IT-enabled business processes. The book establishes AIs fundamental capacity to envision and implement sustainable business transformations.It addresses critical challenges in AI adoption, focusing on two key concerns:AI Interpretability: Models typically optimize for accuracy but struggle to capture real-world costs, especially regarding ethics and fairness. Interpretability features help understand model learning processes, available information, and decision justifications within real-world contexts.Trustworthy AI: Business leaders demand responsible AI solutions that prioritize human needs, safety, and privacy. Researchers are developing methods to enhance trust in AI models and their conclusions to accelerate adoption.Finally, the book presents techniques and approaches for creating sustainable, interpretable, and trustworthy AI models. It explores model-agnostic frameworks and methodologies designed to Trustworthy and Transparent AI, Explainable and Interpretable AI, Responsible AI, Generative AI, Agentic AI, and Efficient and Edge AI.With its comprehensive structure, the book provides a comprehensive examination of AIs potential, its current limitations, and pathways to overcome these challenges for wider adoption. Users expect proper explanation and interpretability of all the decisions being taken by machine and deep learning (ML/DL) algorithms. This book covers key requirements for interpretability and trustworthiness of AI models and how these needs can be fulfilled. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

HRD. Etat : New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

HRD. Etat : New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Buch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Users expect proper explanation and interpretability of all the decisions being taken by machine and deep learning (ML/DL) algorithms. This book covers key requirements for interpretability and trustworthiness of AI models and how these needs can be fulfilled.…