http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Convergence of blockchain and explainable artificial intelligence
Singh, Krishna Kant (Telecommunications professor)
Explainable AI (XAI) is an upcoming research field in the domain of machine learning. This book aims to provide a detailed description of the topics related to XAI and Blockchain. These two technologies can benefit each other, and the research outcomes will benefit society in multiple ways. Existing AI systems make decisions in a black box manner. Explainable AI delineates how an AI system arrived at a particular decision. It inspects the steps and models that are responsible for making a particular decision. It is an upcoming trend that aims at providing explanations to the AI decisions. Blockchain is emerging as an effective technique for XAI. It enables accessibility to digital ledgers amongst the various AI agents. The AI agents collaborate using consensus and decisions are saved on Blocks. These blocks can be traced back but cannot be changed. Thus, the combination of AI with blockchain provides transparency and visibility to all AI decisions. BlockXAI is also being widely used for improving data security and intelligence. The decisions made are consensus based and decentralized leading to highly efficient AI systems. This book also covers topics that present the convergence of Blockchain with explainable AI and will provide researchers, academics, and industry experts with a complete guide to BlockXAI.
Preface xi List of Figures xiii List of Tables xv List of Contributors xvii List of Abbreviations xix 1 BlockXAI: Review of Blockchain for Explainable Artificial Intelligence 1 1.1 Introduction 2 1.2 Blockchain for XAI 2 1.2.1 Blockchain overview 2 1.3 Limitations of Artificial Intelligence 4 1.4 Integration of Blockchain and XAI 5 1.5 Application Area of BlockXAI 8 1.6 Benefits of BlockXAI 9 1.7 Conclusion and Future Work 10 2 XAI decision MODELS: Programming models for decentralized BlockXAI 15 2.1 Introduction 15 2.2 XAI Decision Models 16 2.3 Types of XAI Decision Models 17 2.4 Centralized and Decentralized XAI Decision Models 18 2.5 Decentralized XAI Decision Models 19 2.6 Categories of Decentralized XAI Decision Models 20 3 Blockchain-based frameworks for Explainable AI 23 3.1 Introduction 23 3.2 Blockchain-based XAI Frameworks 24 3.3 AI Decision-making: Explainability and Openness 25 3.4 Challenges Faced 26 3.5 Suggestions 28 4 Explainable Artificial Intelligence (XAI): Notions of Explainability and its Integration with Blockchain 31 4.1 Introduction 32 4.2 Machine Learning and Artificial Neural Networks 33 4.3 Need of XAI 36 4.4 Explainable Artificial Intelligence 38 4.5 XAI and Cognitive Sciences 40 4.6 Explanations and Their Characteristics 41 4.7 Different Types of Approaches to Explain and Interpret 42 4.8 Implemented XAI Applications 43 4.8.1 Recommendation systems 43 4.8.2 Constraint programming 43 4.8.3 Context-aware systems 44 4.8.4 Markov decision processes (MDPs) 44 4.8.5 Explanations in machine learning 44 4.8.6 Deep learning explanation approaches 45 4.9 Blockchain-based Framework for Trustworthy AI 46 4.10 Conclusion 47 5 Human⁸́₂Computer Interaction (HCI): An Advancement in the Development of Computer Technology 53 5.1 Introduction 54 5.2 History of Human-Computer Interaction 56 5.2.1 Present day of human-computer interaction 56 5.2.2 Applications of HCI in different real-life sectors 58 5.3 Industry and Business 58 5.3.1 Accessibility 58 5.3.2 Users who are untrained 58 5.3.3 Other applications 59 5.4 Recent Development in Human-Computer Interaction 59 5.4.1 Dexta haptic gloves 59 5.4.2 Pre-touch sensing 59 5.4.3 Paper ID 59 5.5 Evaluation Methods and Metrics for HCI 60 5.6 Ethical, Legal, and Social Issues of HCI 61 5.7 Future of Human-Computer Interaction 62 5.8 Methodology and Scope of the Research 63 5.9 Recommendations for Future 64 5.10 Conclusion 64 6 Human⁸́₂Computer Interaction and Explainable Artificial Intelligence: For the Effective Use of Technology 69 6.1 Introduction 70 6.2 Overview of HCI and XAI 72 6.3 Methodologies/Tools and Technologies of HCI and XAI Technologies 73 6.4 HCI-based Tools/Technologies 74 6.5 XAI Techniques 75 6.6 Feature Importance 76 6.7 Partial Dependence Plot 76 6.8 Local Interpretable Model-agnostic Explanations (LIME) 77 6.9 SHapley Additive exPlanations (SHAP) 77 6.10 HCI and XAI Systems in Different Real-life Sectors 77 6.11 Shortcomings, Goals/Future Research Opportunities of HCI and XAI 82 6.11.1 Shortcomings of HCI and XAI 82 6.11.2 Shortcomings of XAI 82 6.11.3 Goals/future research opportunities of HCI and XAI 83 6.12 Evaluation Methods for HCI and XAI 87 6.13 Ethical, Legal, and Social Issues of HCI and XAI 88 6.14 Conclusion 89 7 BMRP: Blockchain-Based Medication Reconciliation Process in EHR to Improve Patient Safety 93 7.1 Introduction 94 7.2 Related Work 96 7.3 Background 98 7.4 Medication Reconciliation 99 7.5 Creating Best Possible Medication History 103 7.6 Mitral Valve Replacement 105 7.7 Dataset Description 107 7.8 Methodology 107 7.9 Classification Model 110 7.10 Decision Tree Algorithm 110 7.11 Experiments and Results 112 7.12 Conclusion 113 8 Blockchain-Based IoT Healthcare Data Using Narrow Elliptical Curve with Particle Swarm Optimization (NECPSO) Approach 117 8.1 Introduction 118 8.2 Literature Review 123 8.3 System Design 128 8.4 Result and Discussion 136 8.5 Conclusion 140 9 BlockXAI: Challenges and Future 145 9.1 Introduction 145 9.2 Challenges in Blockchain 146 9.3 Challenges of XAI 147 9.4 Future of Blockchain 148 9.5 Future of XAI 149 9.6 Integration of BlockXAI 149 9.7 Challenges in Integrating Blockchain with XAI 151 9.8 Road Ahead: BlockXAI 152 9.9 Legal and Ethical Issues in BlockXAI 153 9.10 Open Research Problems in BlockXAI 154 9.11 Conclusion 154 Index 157 About the Editors 159.
editors, Akansha Singh, Krishna Kant Singh.
Prof. Akansha Singh, Professor at the School of Computer Science and Engineering, Bennett University, Greater Noida, boasts a comprehensive academic background with a B.Tech, M.Tech, and Ph.D. in Computer Science. Her doctoral studies, conducted at the prestigious IIT Roorkee, were focused on the cutting-edge fields of image processing and machine learning. A prolific author and scholar, Dr. Singh has contributed over 100 research papers and penned several books, alongside a multitude of conference papers. Her editorial expertise is recognized by leading publishers such as Elsevier, Taylor and Francis, and Wiley, where she has edited books on a variety of emerging topics. Her research interests are diverse and influential, spanning image processing, remote sensing, the Internet of Things (IoT), Blockchain and machine learning. Prof. Singh's work in these areas not only advances the field of computer science but also significantly contributes to the broader scientific and technological community. Prof. Dr. Krishna Kant Singh, currently the esteemed Director of Delhi Technical Campus in Greater Noida, India, is a highly experienced educator and researcher in the field of engineering and technology. His academic journey encompasses a B.Tech, M.Tech, and a Ph.D. from the renowned Indian Institute of Technology (IIT) Roorkee, specializing in image processing and machine learning. Dr. Singh's scholarly contributions are remarkable, with over 90 research papers published in prestigious Scopus and SCIE indexed journals. His commitment to knowledge dissemination is further evidenced by his authorship of 25 technical books, which serve as significant resources in the field of engineering and technology. Dr. Singh's dedication to his field is evident through his involvement in various capacities, from research and writing to editorial responsibilities. His work not only advances the field of engineering and technology but also inspires future generations of engineers and researchers.
River Publishers series in digital security and forensics