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[null,"1.","Record Nr.",null,"UNINA990001466890403321"]
[null,null,"Autore",null,"Santesson, Rolf"]
[null,null,"Titolo",null,"Foiicolous Lichens I : a revision of the taxonomy of the obligately foliicolous, lichenized fungi / by Rolf Santesson"]
[null,null,"Pubbl/distr/stampa",null,"Uppsala : Lundequistska Bokhandeln, 1952"]
[null,null,"Descrizione fisica",null,"589 p. : ill. ; 26 cm"]
[null,null,"Locazione",null,"DBV"]
[null,null,"Collocazione",null,"14 V 33"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","in: Symbolae Botanicae Upsalienses XII"]
[null,"2.","Record Nr.",null,"UNINA9911020433703321"]
[null,null,"Autore",null,"Juneja Sapna"]
[null,null,"Titolo",null,"Ethical Decision-Making Using Artificial Intelligence"]
[null,null,"Pubbl/distr/stampa",null,"Newark : , : John Wiley & Sons, Incorporated, , 2025"]
[{"$nulls":4},"©2025"]
[null,null,"ISBN",null,"1-394-27531-5"]
[{"$nulls":4},"1-394-27530-7"]
[null,null,"Edizione",null,"[1st ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource (425 pages)"]
[null,null,"Disciplina",null,"174/.90063"]
[null,null,"Soggetti",null,"Artificial intelligence - Moral and ethical aspects"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Cover -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 Standards, Policies, Ethical Guidelines and Governance in Artificial Intelligence: Insights on the Financial Sector -- 1.1 Introduction -- 1.2 Chatbots in the Financial Industry -- 1.3 Background of the Study -- 1.4 Literature Review -- 1.5 Understanding Bias in Customer Service Chatbots -- 1.5.1 Categorizing Biases in Financial Chatbots -- 1.5.2 Sources and Origins of Bias in Financial Chatbots -- 1.5.3 User Feedback and Bias Detection -- 1.5.4 The Role of Explainability in Unveiling Bias -- 1.6 Impact of Bias in Financial Chatbot Interactions -- 1.6.1 Customer Trust and Satisfaction -- 1.6.2 Perpetuation of Inequalities -- 1.6.3 Reputational Risks for Financial Institutions -- 1.6.4 Regulatory Compliance Challenges -- 1.6.5 Implications for Brand Image -- 1.7 Strategies for Mitigating Bias in Financial Customer Service Chatbots -- 1.7.1 Diverse and Representative Training Data -- 1.7.2 Continuous Monitoring and Iterative Improvement -- 1.7.3 Explainability Features for User Trust -- 1.7.4 Inclusive User Testing -- 1.7.5 Ethical Guidelines and Governance -- 1.7.6 Collaborative Partnerships with Ethical AI Experts -- 1.8 Ethical Considerations and Transparency in Financial Chatbot Firms -- 1.9 Future Directions and Recommendations -- 1.10 Conclusion -- References -- Chapter 2 Domain-Specific AI Algorithms and Models in Decision-Making: An Overview -- 2.1 Introduction -- 2.1.1 Overview of the Role of AI in Decision Making -- 2.1.1.1 The Emergence of Artificial Intelligence: How it is Changing Decision-Making in Several Domains of Economics -- 2.1.1.2 Putting the Power of Artificial Intelligence to Work "]
[{"$nulls":4},"in a Particular Field -- 2.1.1.3 The AI-Assisted Decision-Making Process -- 2.1.1.4 Benefits and Future of AI-Powered Decision-Making."]
[{"$nulls":4},"2.1.2 Importance of Domain-Specific Approaches -- 2.1.2.1 Advantages of Domain-Specific AI -- 2.1.2.2 Instances of Domain-Specific AI in Action -- 2.1.2.3 General AI versus Domain-Specific AI: Powering Intelligent Decisions -- 2.2 Understanding Domain-Specific Decision Making -- 2.2.1 Bridging the Gap: Explainable AI for Effective Collaboration between Machine Learning and Domain Expertise -- 2.3 Building Blocks of AI for Decision-Making -- 2.3.1 Overview of AI Approaches -- 2.3.2 Machine Learning for Data-Driven Decision Generating -- 2.3.3 Knowledge-Based Systems for Rule-Based Decision-Making -- 2.3.4 Reinforcement Learning in Dynamic Environments -- 2.4 Domain-Specific AI: Revolutionizing Industries -- 2.4.1 Healthcare -- 2.4.1.1 The Importance of Patient-Centered Design in Regulating Large Language Models or Generative AI -- 2.4.1.2 XAI in Biomedicine: A Post-Pandemic Surge for Trustworthy AI in Healthcare Delivery -- 2.4.2 Finance -- 2.4.2.1 Explainable AI: A Path Toward Trustworthy and Ethical Applications of Machine Learning in Finance -- 2.4.2.2 Learning Machines, Evolving Markets: The Need for Adaptable Generative AI in Finance -- 2.4.3 Manufacturing -- 2.4.3.1 The Rise of Generative AI: A Call for Responsible AI Frameworks in MSME Manufacturing -- 2.4.3.2 Guiding the Future of Manufacturing: Responsible AI as a Cornerstone for Sustainable and Ethical Production -- 2.4.4 Transportation -- 2.4.4.1 Revolutionizing Urban Mobility: The Power of Machine Learning and AI in Smart City Transportation -- 2.4.4.2 AI Revolutionizes Transportation: Boosting Efficiency, Safety, and New Business Opportunities -- 2.4.5 Agriculture -- 2.4.5.1 Cultivating a Sustainable Future: How AI and Big Data are Revolutionizing Precision Agriculture."]
[{"$nulls":4},"2.4.5.2 AI in the Fields: From Precision Irrigation to Smart Robots, How Artificial Intelligence Is Revolutionizing Agribusiness -- 2.4.6 Retail -- 2.4.6.1 The Generative Retail Revolution: How AI is Personalizing Customer Experience, Optimizing Inventory, and Driving Sales -- 2.4.6.2 The Future of Retail: Leveraging AI for Efficiency and Personalization while Navigating Data Privacy and Ethical Challenges -- 2.4.7 Domain-Specific AI: A Comparative Analysis -- 2.5 Ethical and Societal Implications -- 2.6 Future Directions and Emerging Trends -- 2.7 Conclusion -- References -- Chapter 3 Role of AI in Decision-Making . A Comprehensive Study -- 3.1 Introduction -- 3.2 Need of AI-Based Decision-Making System -- 3.3 Major Obstacle for AI-Based Decision-Making System -- 3.4 Applications of AI-Based Decision-Making System -- 3.5 Case Study: AIDMS for Age-Related Macular Degeneration (AMD) -- 3.6 Conclusion and Future Directions -- References -- Chapter 4 Ethical Challenges in AI Decision-Making: From the User's Perspective -- 4.1 Introduction -- 4.1.1 Ethical Principles in AI -- 4.1.2 The Role of Data in AI Decision-Making -- 4.2 Public Perception towards AI -- 4.3 Ethical Dilemmas of AI -- 4.4 Emerging Issues that are Prevailing in the Current World -- 4.4.1 Case Studies -- 4.4.2 Collaboration and Stakeholder Involvement -- 4.5 Future Considerations -- 4.5.1 Conclusion -- References -- Chapter 5 Ethical Decision-Making in Yoga Posture Detection through AI: Fostering Responsible Technology Integration -- 5.1 Introduction -- 5.1.1 About Yoga -- 5.1.1.1 Advantages and Disadvantages of Yoga -- 5.1.2 Posture Detection System -- 5.1.2.1 Components of Posture Detection System -- 5.1.2.2 Process of Posture Detection System -- 5.1.2.3 Applications of Posture Detection System -- 5.1.2.4 Advantages and Disadvantages of Posture Detection System."]
[{"$nulls":4},"5.1.3 Ethical Decision-Making in Yoga Posture Detection through AI -- "]
[{"$nulls":4},"5.2 Literature Review -- 5.3 Technologies Used -- 5.3.1 MediaPipe -- 5.3.2 OpenCV (Open-Source Computer Vision Library) -- 5.4 Dataset Used -- 5.5 Methodology -- 5.5.1 How Does It Work? -- 5.6 Conclusion -- References -- Chapter 6 Ethical AI: A Design of an Integrated Framework towards Intelligent Decision-Making in Stock Control -- 6.1 Introduction -- 6.1.1 The Effect of Artificial Intelligence on Controlling Inventory -- 6.1.2 Process of Evolution and Development in Stock Control -- 6.2 Benefits and Impact of AI on Inventory Control -- 6.2.1 Moral Considerations in AI-Primarily Based Selection Making -- 6.3 Best Practices for Implementing AI for Stock Management in E-Commerce -- 6.3.1 Consideration in Statistics and Statistics Safety -- 6.3.2 How AI Enables Stock Administration for Important Corporations -- 6.3.3 Synthetic Intelligence in Inventory Administration: Destiny Styles and Extension -- 6.3.4 Inventory Control with Predictive Renovation -- 6.4 Formulation of Proposed Model -- 6.4.1 Framework Discussion -- 6.4.2 Assumptions and Notations -- 6.4.3 Proposed Mathematical Model -- 6.4.4 Example -- 6.4.5 Sensitivity Analysis -- 6.5 Conclusion -- References -- Chapter 7 Integrating Machine Learning and Data Ethics: Frameworks for Intelligent Ethical Decision-Making -- 7.1 Introduction -- 7.2 Concept of Machine Learning and Data Ethics -- 7.3 Importance of ML and AI in Design Making -- 7.4 Defining an Intelligent Decision-Making Support System -- 7.5 Transformation of the Decision-Making System to Intelligent Decision-Making Support -- 7.6 Architecture Framework -- 7.6.1 Components of the IDSS Architecture -- 7.7 Conceptual Framework -- 7.7.1 Core Concepts -- 7.7.2 Components of the Conceptual Framework -- 7.7.3 Block Diagram of the Conceptual Framework."]
[{"$nulls":4},"7.7.4 Principles of Framework -- 7.7.4.1 Tools Used in IDMSS -- 7.7.4.2 Data Processing Tools -- 7.7.4.3 Machine Learning Frameworks -- 7.7.4.4 Cloud Computing Platforms -- 7.7.5 Analyzing Different Tools -- 7.7.6 Data Processing Tools -- 7.7.7 Machine Learning Frameworks -- 7.7.8 Convolutional Neural Networks (CNNs) -- 7.7.9 Recurrent Neural Networks (RNNs) -- 7.7.10 Cloud Computing Platforms -- 7.8 Cloud-Based Scalability with Auto Scaling -- 7.9 Case Study of Complex Problem Using Framework -- 7.10 Algorithm and Coding Analysis -- 7.11 Results and Impact Analysis -- 7.12 Conclusion -- References -- Chapter 8 Importance of Human Loop in AI-Based Decision-Making: Strengthening the Ethical Perspective -- 8.1 Introduction -- 8.1.1 Human-in-the-Loop -- 8.2 Human Interaction with AI Platform -- 8.3 Human and Machine Ethical Annotation -- 8.4 Exploring AI with Human-in-the-Loop Technique -- 8.4.1 AI-Ethical Module -- 8.4.2 Role of HITL in Ethical Decision-Making -- 8.5 Creating Ethical AI Using HTIL Technique -- 8.5.1 Distributed Ethical Decision System -- 8.5.2 Viability and Advantages of Decision-Making Using Ethical AI -- 8.5.3 Problem Statement -- 8.6 Conclusion -- References -- Chapter 9 AI in Finance and Business: Novel Method for Human Resource Recommendation Using Improved Gradient Boosting Tree Model -- 9.1 Introduction -- 9.2 Literature Review -- 9.2.1 Deep Learning Approach -- 9.2.2 Gradient Boosting Tree -- 9.2.3 Convolutional Neural Network -- 9.2.3.1 Layer of Convolution -- 9.2.3.2 Pool Layer -- 9.2.3.3 Active Layer -- 9.2.3.4 Full Connection Layer -- 9.2.4 Deeper Learning Organizational Techniques -- 9.3 The Proposed Model -- 9.4 Evaluation of the Impact of the Technology -- 9.4.1 Data Set -- 9.4.1.1 Evaluation Criteria -- 9.5 Conclusion -- References -- Chapter 10 Comprehensive View from Ethics to AI Ethics: With Multifaceted Dimensions."]
[{"$nulls":4},"10.1 Introduction."]
[null,null,null,"Sommario/riassunto","Ethical Decision-Making Using Artificial Intelligence: Challenges, Solutions, and Applications gives invaluable insights into the ethical complexities of artificial intelligence, empowering the navigation of critical decisions that shape our future in an era where AI's influence on society is rapidly expanding. The significant impact of artificial intelligence on society cannot be overstated in a time of lightning-fast technical development and growing integration of AI into our daily lives. A new frontier of human potential has emerged with the development and application of AI technologies, pushing the limits of what is possible in the areas of innovation and efficiency. AI systems are increasingly trusted with complicated decisions that affect our security, well-being, and the fundamental foundation of our societies as they develop in intelligence and autonomy. These choices have substantial repercussions for both individuals and communities in a wide range of fields, including healthcare, finance, criminal justice, and transportation. The necessity for moral direction and deliberate decision-making procedures is critical as AI systems develop and become more independent. Ethical Decision-Making Using Artificial Intelligence: Challenges, Solutions, and Applications examines the complex relationship between artificial intelligence and the moral principles that guide its application. This book addresses fundamental concerns surrounding AI ethics, namely what moral standards ought to direct the creation and use of AI systems. In order to promote responsible AI development that is consistent with human values and goals, this book's goal is to equip readers with the knowledge and skills they need to traverse the ethical landscape of AI decision-making."]