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Author: Mayuri Mehta
Publisher: Springer Nature
ISBN: 3031128079
Category : Technology & Engineering
Languages : en
Pages : 273
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Book Description
This book presents an overview and several applications of explainable artificial intelligence (XAI). It covers different aspects related to explainable artificial intelligence, such as the need to make the AI models interpretable, how black box machine/deep learning models can be understood using various XAI methods, different evaluation metrics for XAI, human-centered explainable AI, and applications of explainable AI in health care, security surveillance, transportation, among other areas. The book is suitable for students and academics aiming to build up their background on explainable AI and can guide them in making machine/deep learning models more transparent. The book can be used as a reference book for teaching a graduate course on artificial intelligence, applied machine learning, or neural networks. Researchers working in the area of AI can use this book to discover the recent developments in XAI. Besides its use in academia, this book could be used by practitioners in AI industries, healthcare industries, medicine, autonomous vehicles, and security surveillance, who would like to develop AI techniques and applications with explanations.
Author: Mayuri Mehta
Publisher: Springer Nature
ISBN: 3031128079
Category : Technology & Engineering
Languages : en
Pages : 273
Get Book Here
Book Description
This book presents an overview and several applications of explainable artificial intelligence (XAI). It covers different aspects related to explainable artificial intelligence, such as the need to make the AI models interpretable, how black box machine/deep learning models can be understood using various XAI methods, different evaluation metrics for XAI, human-centered explainable AI, and applications of explainable AI in health care, security surveillance, transportation, among other areas. The book is suitable for students and academics aiming to build up their background on explainable AI and can guide them in making machine/deep learning models more transparent. The book can be used as a reference book for teaching a graduate course on artificial intelligence, applied machine learning, or neural networks. Researchers working in the area of AI can use this book to discover the recent developments in XAI. Besides its use in academia, this book could be used by practitioners in AI industries, healthcare industries, medicine, autonomous vehicles, and security surveillance, who would like to develop AI techniques and applications with explanations.
Author: I. Tiddi
Publisher: IOS Press
ISBN: 1643680811
Category : Computers
Languages : en
Pages : 314
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Book Description
The latest advances in Artificial Intelligence and (deep) Machine Learning in particular revealed a major drawback of modern intelligent systems, namely the inability to explain their decisions in a way that humans can easily understand. While eXplainable AI rapidly became an active area of research in response to this need for improved understandability and trustworthiness, the field of Knowledge Representation and Reasoning (KRR) has on the other hand a long-standing tradition in managing information in a symbolic, human-understandable form. This book provides the first comprehensive collection of research contributions on the role of knowledge graphs for eXplainable AI (KG4XAI), and the papers included here present academic and industrial research focused on the theory, methods and implementations of AI systems that use structured knowledge to generate reliable explanations. Introductory material on knowledge graphs is included for those readers with only a minimal background in the field, as well as specific chapters devoted to advanced methods, applications and case-studies that use knowledge graphs as a part of knowledge-based, explainable systems (KBX-systems). The final chapters explore current challenges and future research directions in the area of knowledge graphs for eXplainable AI. The book not only provides a scholarly, state-of-the-art overview of research in this subject area, but also fosters the hybrid combination of symbolic and subsymbolic AI methods, and will be of interest to all those working in the field.
Author: Wojciech Samek
Publisher: Springer Nature
ISBN: 3030289540
Category : Computers
Languages : en
Pages : 435
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Book Description
The development of “intelligent” systems that can take decisions and perform autonomously might lead to faster and more consistent decisions. A limiting factor for a broader adoption of AI technology is the inherent risks that come with giving up human control and oversight to “intelligent” machines. For sensitive tasks involving critical infrastructures and affecting human well-being or health, it is crucial to limit the possibility of improper, non-robust and unsafe decisions and actions. Before deploying an AI system, we see a strong need to validate its behavior, and thus establish guarantees that it will continue to perform as expected when deployed in a real-world environment. In pursuit of that objective, ways for humans to verify the agreement between the AI decision structure and their own ground-truth knowledge have been explored. Explainable AI (XAI) has developed as a subfield of AI, focused on exposing complex AI models to humans in a systematic and interpretable manner. The 22 chapters included in this book provide a timely snapshot of algorithms, theory, and applications of interpretable and explainable AI and AI techniques that have been proposed recently reflecting the current discourse in this field and providing directions of future development. The book is organized in six parts: towards AI transparency; methods for interpreting AI systems; explaining the decisions of AI systems; evaluating interpretability and explanations; applications of explainable AI; and software for explainable AI.
Author: Mohammad Amir Khusru Akhtar
Publisher: Springer Nature
ISBN: 3031664892
Category :
Languages : en
Pages : 381
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Book Description
Author: Rajanikanth Aluvalu
Publisher: Springer Nature
ISBN: 9819737052
Category :
Languages : en
Pages : 287
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Book Description
Author: Uma Maheswari V
Publisher: CRC Press
ISBN: 1040109608
Category : Computers
Languages : en
Pages : 319
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Book Description
This book covers the fundamental concepts of private AI and its applications. It also covers fusion of Private AI with cutting-edge technologies like cloud computing, federated learning and computer vision. Security Models and Applications for Sustainable Development Using Private AI reviews various encryption algorithms used for providing security in private AI. It discusses the role of training machine learning and Deep learning technologies in private AI. The book provides case studies of using private AI in various application areas such as purchasing, education, entertainment, medical diagnosis, predictive care, conversational personal assistants, wellness apps, early disease detection, and recommendation systems. The authors provide additional knowledge to handling the customer’s data securely and efficiently. It also provides multi-model dataset storage approaches along with the traditional approaches like anonymization of data and differential privacy mechanisms. The target audience includes undergraduate and postgraduate students in Computer Science, Information technology, Electronics and Communication Engineering and related disciplines. This book is also a one stop reference point for professionals, security researchers, scholars, various government agencies and security practitioners, and experts working in the cybersecurity Industry specifically in the R & D division.
Author: Denis Rothman
Publisher: Packt Publishing Ltd
ISBN: 1800202768
Category : Computers
Languages : en
Pages : 455
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Book Description
Resolve the black box models in your AI applications to make them fair, trustworthy, and secure. Familiarize yourself with the basic principles and tools to deploy Explainable AI (XAI) into your apps and reporting interfaces. Key FeaturesLearn explainable AI tools and techniques to process trustworthy AI resultsUnderstand how to detect, handle, and avoid common issues with AI ethics and biasIntegrate fair AI into popular apps and reporting tools to deliver business value using Python and associated toolsBook Description Effectively translating AI insights to business stakeholders requires careful planning, design, and visualization choices. Describing the problem, the model, and the relationships among variables and their findings are often subtle, surprising, and technically complex. Hands-On Explainable AI (XAI) with Python will see you work with specific hands-on machine learning Python projects that are strategically arranged to enhance your grasp on AI results analysis. You will be building models, interpreting results with visualizations, and integrating XAI reporting tools and different applications. You will build XAI solutions in Python, TensorFlow 2, Google Cloud’s XAI platform, Google Colaboratory, and other frameworks to open up the black box of machine learning models. The book will introduce you to several open-source XAI tools for Python that can be used throughout the machine learning project life cycle. You will learn how to explore machine learning model results, review key influencing variables and variable relationships, detect and handle bias and ethics issues, and integrate predictions using Python along with supporting the visualization of machine learning models into user explainable interfaces. By the end of this AI book, you will possess an in-depth understanding of the core concepts of XAI. What you will learnPlan for XAI through the different stages of the machine learning life cycleEstimate the strengths and weaknesses of popular open-source XAI applicationsExamine how to detect and handle bias issues in machine learning dataReview ethics considerations and tools to address common problems in machine learning dataShare XAI design and visualization best practicesIntegrate explainable AI results using Python modelsUse XAI toolkits for Python in machine learning life cycles to solve business problemsWho this book is for This book is not an introduction to Python programming or machine learning concepts. You must have some foundational knowledge and/or experience with machine learning libraries such as scikit-learn to make the most out of this book. Some of the potential readers of this book include: Professionals who already use Python for as data science, machine learning, research, and analysisData analysts and data scientists who want an introduction into explainable AI tools and techniquesAI Project managers who must face the contractual and legal obligations of AI Explainability for the acceptance phase of their applications
Author: Constantine Stephanidis
Publisher: CRC Press
ISBN: 1040088988
Category : Computers
Languages : en
Pages : 488
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Book Description
This book covers user experience methods and tools in designing user‐friendly products and servicesby encompassing widely utilized successful methods, including elicitation, analysis and establishment of requirements, collaborative idea generation with design teams and intended users, prototype testing and evaluation of the user experience through empirical and non‐empirical means. This book • Provides methods and tools tailored for each stage of the design process. • Discusses methods for the active involvement of users in the human‐centered design process. • Equips readers with an effective toolset for use throughout the design process, ensuring that what is created aligns with user needs and desires. • Covers a wide array of research and evaluation methods employed in HCI, from the initiation of the human‐centered development cycle to its culmination. This book is a fascinating read for individuals interested in Human-Computer Interaction research and applications.
Author: Lakshmi D
Publisher: CRC Press
ISBN: 1040038832
Category : Technology & Engineering
Languages : en
Pages : 335
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Book Description
This book presents innovative research works to automate, innovate, design, and deploy AI fo real-world applications. It discusses AI applications in major cutting-edge technologies and details about deployment solutions for different applications for sustainable development. The application of Blockchain techniques illustrates the ways of optimisation algorithms in this book. The challenges associated with AI deployment are also discussed in detail, and edge computing with machine learning solutions is explained. This book provides multi-domain applications of AI to the readers to help find innovative methods towards the business, sustainability, and customer outreach paradigms in the AI domain. • Focuses on virtual machine placement and migration techniques for cloud data centres • Presents the role of machine learning and meta-heuristic approaches for optimisation in cloud computing services • Includes application of placement techniques for quality of service, performance, and reliability improvement • Explores data centre resource management, load balancing and orchestration using machine learning techniques • Analyses dynamic and scalable resource scheduling with a focus on resource management The reference work is for postgraduate students, professionals, and academic researchers in computer science and information technology.
Author: Constantine Stephanidis
Publisher: CRC Press
ISBN: 1040318606
Category : Computers
Languages : en
Pages : 2935
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Book Description
The pervasive influence of technology continuously shapes our daily lives. From smartphones to smart homes, technology is revolutionizing the way we live, work and interact with each other. Human-computer interaction (HCI) is a multidisciplinary research field focusing on the study of people interacting with information technology and plays a critical role in the development of computing systems that work well for the people using them, ensuring the seamless integration of interactive systems into our technologically driven lifestyles. The book series contains six volumes providing extensive coverage of the field, wherein each one addresses different theoretical and practical aspects of the HCI discipline. Readers will discover a wealth of information encompassing the foundational elements, state-of-the-art review in established and emerging domains, analysis of contemporary advancements brought about by the evolution of interactive technologies and artificial intelligence, as well as the emergence of diverse societal needs and application domains. These books: · Showcase the pivotal role of HCI in designing interactive applications across a diverse array of domains. · Explore the dynamic relationship between humans and intelligent environments, with a specific emphasis on the role of Artificial Intelligence (AI) and the Internet of Things (IoT). · Provide an extensive exploration of interaction design by examining a wide range of technologies, interaction techniques, styles and devices. · Discuss user experience methods and tools for the design of user-friendly products and services. · Bridge the gap between software engineering and human-computer interaction practices for usability, inclusion and sustainability. These volumes are an essential read for individuals interested in human-computer interaction research and applications.
