Explainable Artificial Intelligence (XAI) : Concepts, Enabling Tools, Technologies and Applications

• Autore: Raj Pethuru
• Pubblicazione: Stevenage : , : Institution of Engineering & Technology, , 2023; ©2023
• Edizione: 1st ed.
• Descrizione fisica: 1 online resource (465 pages)
• Disciplina: 006.3
• Soggetto topico: Artificial intelligence; Machine learning
• Altri autori: KöseUtku SakthivelUsha NagarajanSusila AsirvadamVijanth Sagayan
• Nota di contenuto: Intro -- Title -- Copyright -- Contents -- About the editors -- Preface -- 1 An overview of past and present progressions in XAI -- 1.1 Introduction -- 1.2 Background study -- 1.2.1 Key-related ideas of XAI -- 1.3 Overview of XAI -- 1.4 History of XAI -- 1.5 Top AI patterns -- 1.6 Conclusion -- References -- 2 Demystifying explainable artificial intelligence (EAI) -- 2.1 Introduction -- 2.1.1 An overview of artificial intelligence -- 2.1.2 Introduction to explainable AI -- 2.2 Concept of XAI -- 2.3 Explainable AI (EAI) architecture -- 2.4 Learning techniques -- 2.5 Demystifying EAI methods -- 2.5.1 Clever Hans -- 2.5.2 Different users and goals in EAI -- 2.5.3 EAI as quality assurance -- 2.6 Implementation: how to create explainable solutions -- 2.6.1 Method taxonomy -- 2.6.2 Rules - intrinsic local explanations -- 2.6.3 Prototypes -- 2.6.4 Learned representation -- 2.6.5 Partial dependence plot - global post-hoc explanations -- 2.6.6 Feature attribution (importance) -- 2.7 Applications -- 2.8 Conclusion -- References -- 3 Illustrating the significance of explainable artificial intelligence (XAI) -- 3.1 Introduction -- 3.2 The growing power of AI -- 3.3 The challenges and concerns of AI -- 3.4 About the need for AI explainability -- 3.5 The importance of XAI -- 3.6 The importance of model interpretation -- 3.6.1 Model transparency -- 3.6.2 Start with interpretable algorithms -- 3.6.3 Standard techniques for model interpretation -- 3.6.4 ROC curve -- 3.6.5 Focus on feature importance -- 3.6.6 Partial dependence plots (PDPs) -- 3.6.7 Global surrogate models -- 3.6.8 Criteria for ML model interpretation methods -- 3.7 Briefing feature importance scoring methods -- 3.8 Local interpretable model-agnostic explanations (LIMEs) -- 3.9 SHAP explainability algorithm -- 3.9.1 AI trust with symbolic AI. ; 3.10 The growing scope of XAI for the oil and gas industry -- 3.10.1 XAI for the oil and gas industry -- 3.11 Conclusion -- Bibliography -- 4 Inclusion of XAI in artificial intelligence and deep learning technologies -- 4.1 Introduction -- 4.2 What is XAI? -- 4.3 Why is XAI important? -- 4.4 How does XAI work? -- 4.5 Role of XAI in machine learning and deep learning algorithm -- 4.6 Applications of XAI in machine learning in deep learning -- 4.7 Difference between XAI and AI -- 4.8 Challenges in XAI -- 4.9 Advantages of XAI -- 4.10 Disadvantages of XAI -- 4.11 Future scope of XAI -- 4.12 Conclusion -- References -- 5 Explainable artificial intelligence: tools, platforms, and new taxonomies -- 5.1 Introduction -- 5.2 ML-based systems and awareness -- 5.3 Challenges of the time -- 5.3.1 Requirement of explainability -- 5.3.2 Impact of high-stake decisions -- 5.3.3 Concerns of society -- 5.3.4 Regulations and interpretability issue -- 5.4 State-of-the-art approaches -- 5.5 Assessment approaches -- 5.6 Drivers for XAI -- 5.6.1 Tools and frameworks -- 5.7 Discussion -- 5.7.1 For researchers outside of computer science: taxonomies -- 5.7.2 Taxonomies and reviews focusing on specific aspects -- 5.7.3 Fresh perspectives on taxonomy -- 5.7.4 Taxonomy levels at new levels -- 5.8 Conclusion -- References -- 6 An overview of AI platforms, frameworks, libraries, and processes -- 6.1 Introduction to AI -- 6.2 Role of AI in the 21st century -- 6.2.1 The 2000s -- 6.2.2 The 2010s -- 6.2.3 The future -- 6.3 How AI transformed the world -- 6.3.1 Transportation -- 6.3.2 Finance -- 6.3.3 Healthcare -- 6.3.4 Intelligent cities -- 6.3.5 Security -- 6.4 AI process -- 6.5 TensorFlow -- 6.5.1 Installation -- 6.5.2 TensorFlow basics -- 6.6 Scikit learn -- 6.6.1 Features -- 6.6.2 Installation -- 6.6.3 Scikit modeling -- 6.6.4 Data representation in scikit -- 6.7 Keras. ; 6.7.1 Features -- 6.7.2 Building a model in Keras -- 6.7.3 Applications of Keras -- 6.8 Open NN -- 6.8.1 Application -- 6.8.2 RNN -- 6.9 Theano -- 6.9.1 An overview -- 6.10 Why go for Theano Python library? -- 6.10.1 PROS -- 6.10.2 CONS -- 6.11 Basics of Theano -- 6.11.1 Subtracting two scalars -- 6.11.2 Adding two scalars -- 6.11.3 Adding two matrices -- 6.11.4 Logistic function -- References -- 7 Quality framework for explainable artificial intelligence (XAI) and machine learning applications -- 7.1 Introduction -- 7.2 Background -- 7.3 Integrated framework for AI applications development -- 7.4 AI systems characteristics vs. SE best practices -- 7.4.1 Explainable AI characteristics -- 7.5 ML lifecycle (model, data-oriented, and data analytics-oriented lifecycle) -- 7.6 AI/ML requirements engineering -- 7.7 Software effort estimation for AMD, RL, and NLP systems -- 7.7.1 Modified COCOMO model for AI, ML, and NLP applications and apps -- 7.8 Software engineering framework for AI and ML (SEF4 AI and ML) applications -- 7.9 Reference Architecture for AI & -- ML -- 7.10 Evaluation of Reference Architecture (REF) for AI & -- ML: explainable Chatbot case study -- 7.11 Conclusions and further research -- References -- 8 Methods for explainable artificial intelligence -- 8.1 Preliminarily study -- 8.2 Importance of XAI for human-interpretable models -- 8.3 Overview of XAI techniques -- 8.4 Taxonomy of popular XAI methods -- 8.4.1 Backpropagation-based methods -- 8.4.2 Perturbation methods -- 8.4.3 Influence methods -- 8.4.4 Knowledge extraction -- 8.4.5 Concept methods -- 8.4.6 Visualization methods -- 8.4.7 Example-based explanation -- 8.5 Conclusion -- References -- 9 Knowledge representation and reasoning (KRR) -- 9.1 Introduction -- 9.2 Methodology -- 9.2.1 Reference model -- 9.2.2 Ontologies -- 9.2.3 Knowledge graphs. ; 9.2.4 Semantic web technologies -- 9.2.5 ML -- 9.2.6 Tools and techniques -- 9.3 Results and discussion -- 9.3.1 Case study: using different techniques for representing medical knowledge [7] -- 9.3.2 Case study: using different techniques for representing academic knowledge [8] -- 9.3.3 Case study: using different techniques for representing farmer knowledge [9] -- 9.3.4 Case study: social media knowledge representation techniques [10] -- 9.3.5 Case study: using different techniques for representing cyber security knowledge [11] -- 9.4 Conclusion and future work -- References -- 10 Knowledge visualization: AI integration with 360-degree dashboards -- 10.1 Introduction -- 10.2 Information visualization vs. knowledge visualization -- 10.3 Knowledge visualization in design thinking -- 10.4 Visualization in transferring knowledge -- 10.5 The knowledge visualization model -- 10.5.1 Knowledge visualization framework -- 10.6 Formats and examples of knowledge visualization -- 10.6.1 Conceptual diagrams -- 10.6.2 Visual metaphors -- 10.6.3 Knowledge animation -- 10.6.4 Knowledge maps -- 10.6.5 Knowledge domain visualization -- 10.7 Types and usage of knowledge visualization tools -- 10.8 Knowledge visualization templates -- 10.8.1 Mind maps -- 10.8.2 Swimlane diagrams -- 10.8.3 Matrix diagrams -- 10.8.4 Flowcharts -- 10.8.5 Concept maps -- 10.8.6 Funnel charts or diagrams -- 10.9 Visualization in machine learning -- 10.9.1 Decision trees -- 10.9.2 Decision graph -- 10.10 Conclusion -- References -- 11 Empowering machine learning with knowledge graphs for the semantic era -- 11.1 Introduction -- 11.2 Tending towards digitally transformed enterprises -- 11.3 The emergence of KGs -- 11.4 Briefing the concept of KGs -- 11.5 Formalizing KGs -- 11.6 Creating custom KGs -- 11.7 Characterizing KGs -- 11.8 Use cases of KGs -- 11.9 ML and KGs. ; 11.10 KGs for explainable and responsible AI -- 11.11 Stardog enterprise KG platform -- 11.12 What CANNOT be considered a KG? -- 11.13 Conclusion -- Bibliography -- 12 Enterprise knowledge graphs using ensemble learning and data management -- 12.1 Introduction -- 12.2 Current ensemble model learning -- 12.2.1 Bagging -- 12.2.2 Boosting -- 12.2.3 Random Forest -- 12.3 Related work and literature review -- 12.4 Methodology -- 12.4.1 Enhanced ensemble model framework -- 12.4.2 Training and testing datasets -- 12.4.3 Enhanced ensemble model and algorithm -- 12.5 Experimental setup and enterprise dataset -- 12.5.1 Ensemble models performance evaluation using enterprise knowledge graph -- 12.5.2 Tree classification as knowledge graph -- 12.6 Result and discussion -- 12.7 Conclusion -- References -- 13 Illustrating graph neural networks (GNNs) and the distinct applications -- 13.1 Introduction -- 13.2 Briefing the distinctions of graphs -- 13.3 The challenges -- 13.4 ML algorithms -- 13.5 DL algorithms -- 13.6 The emergence of GNNs -- 13.7 Demystifying DNNs on graph data -- 13.8 GNNs: the applications -- 13.9 The challenges for GNNs -- 13.10 Conclusion -- Bibliography -- 14 AI applications-computer vision and natural language processing -- 14.1 Object recognition -- 14.2 AI-powered video analytics -- 14.3 Contactless payments -- 14.4 Foot tracking -- 14.5 Animal detection -- 14.6 Airport facial recognition -- 14.7 Autonomous driving -- 14.8 Video surveillance -- 14.9 Healthcare medical detection -- 14.10 Computer vision in agriculture -- 14.10.1 Drone-based crop monitoring -- 14.10.2 Yield analysis -- 14.10.3 Smart systems for crop grading and sorting -- 14.10.4 Automated pesticide spraying -- 14.10.5 Phenotyping -- 14.10.6 Forest information -- 14.11 Computer vision in transportation -- 14.11.1 Safety and driver assistance -- 14.11.2 Traffic control. ; 14.11.3 Driving autonomous vehicles.
• Sommario/riassunto: This book focuses on Explainable AI (XAI) concepts, tools, frameworks, techniques and applications. It introduces knowledge graphs (KG) to support the need for trust and transparency into the functioning of AI systems, and shows how Intelligent applications can be used to greater effect in finance and healthcare.
• Titolo autorizzato: Explainable Artificial Intelligence (XAI)
• ISBN: 1-83724-425-1; 1-5231-6305-4; 1-83953-696-9
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9911007174203321
• Lo trovi qui: Univ. Federico II
• Serie: Computing and Networks Series