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Submit a ProposalEthical Decision-Making Using Artificial Intelligence
 | Challenges, Solutions, and Applications Edited by Sapna Juneja, Rajesh Kumar Dhanaraj, Abhinav Juneja, Maalathy Sathyamoorthy, and Asadullah Shaikh Copyright: 2025 | Expected Pub Date:2025// ISBN: 9781394275281 | Hardcover | 420 pages Price: $225 USD  |
One Line Description
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 AIs influence on society is rapidly expanding.
Audience
Academics, research scholars, and IT professionals concerned with the ethical implications of applying AI for decision-making
Academics, research scholars, and IT professionals concerned with the ethical implications of applying AI for decision-making
Description
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.
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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.
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Author / Editor Details
Sapna Juneja, PhD is a professor and Associate Dean of Research and Development in the Department of Computer Science and Engineering with the KIET Group of Institutions, with over 17 years of experience. She has published six patents and various research articles in renowned national and international journals. Her research interests include software engineering, computer networks, operating systems, database management systems, and artificial intelligence.
Sapna Juneja, PhD is a professor and Associate Dean of Research and Development in the Department of Computer Science and Engineering with the KIET Group of Institutions, with over 17 years of experience. She has published six patents and various research articles in renowned national and international journals. Her research interests include software engineering, computer networks, operating systems, database management systems, and artificial intelligence.
Rajesh Kumar Dhanaraj, PhD is a professor at Symbiosis International University. He has authored and edited over 50 books, numerous book chapters, and over 100 articles in refereed international journals, in addition to 21 patents. His research interests include machine learning, cyber-physical systems, and wireless sensor networks.
Abhinav Juneja, PhD is a professor and the Head of the Department of Computer Science and Information Technology with the KIET Group of Institutions, with over 21 years of teaching experience. He has edited two books and has over 55 publications in national and international books, journals, and conferences. His research focuses on machine learning and Internet of Things.
Malathy Sathyamoorthy, PhD is an assistant professor in the Department of Information Technology at the KPR Institute of Engineering and Technology. She has published over 25 research papers in international journals. 22 papers in international conferences, two patents, four book chapters, and one book. His research interests include wireless sensor networks, networking, security, and machine learning.
Asadullah Shaikh, PhD is a professor, the Head of Research and Graduate Studies, and the coordinator for seminars and training with the College of Computer Science and Information Systems, at Najran University. He has over 170 publications in international journals and conferences. His research interests include Unified Modeling Language model verification and class diagrams verification with Object Constraint Language constraints for complex models, formal verification, and feedback techniques for unsatisfiable UML and OCL class diagrams.
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Table of Contents
Preface
1. Standards, Policies, Ethical Guidelines and Governance in Artificial Intelligence: Insights on the Financial Sector
Purohit S. and Arora, R.
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
2. Domain-Specific AI Algorithms and Models in Decision-Making: An Overview
P. Kanaga Priya, A. Reethika, Malathy Sathyamoorthy and Rajesh Kumar Dhanaraj
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 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
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
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
3. Role of AI in Decision-Making – A Comprehensive Study
Rohit Vashisht, Sonia Deshmukh and Ashima Arya
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
4. Ethical Challenges in AI Decision‑Making: From the User’s Perspective
M. Nalini, S. Sandhya and S. Shiwani
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
5. Ethical Decision-Making in Yoga Posture Detection through AI: Fostering Responsible Technology Integration
Ishita Jain, Riya Srivastava, Vanshita Srivastava, Vanshika Sinha and Abhinav Juneja
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
5.1.3 Ethical Decision-Making in Yoga Posture Detection through AI
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
6. Ethical AI: A Design of an Integrated Framework towards Intelligent Decision-Making in Stock Control
Mini Verma and Palak Gupta
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
7. Integrating Machine Learning and Data Ethics: Frameworks for Intelligent Ethical Decision-Making
Karishma Sharma, Deepa Gupta, Mukul Gupta and Rajesh Dhanaraj
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
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
8. Importance of Human Loop in AI-Based Decision-Making: Strengthening the Ethical Perspective
A. Reethika, P. Kanaga Priya, Malathy Sathyamoorthy and Rajesh Kumar Dhanaraj
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
9. AI in Finance and Business: Novel Method for Human Resource Recommendation Using Improved Gradient Boosting Tree Model
Mahima Shanker Pandey, Abhishek Singh, Bihari Nandan Pandey, Aparna Sharma
and Prashant Upadhyay
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
10. Comprehensive View from Ethics to AI Ethics: With Multifaceted Dimensions
Kanika Budhiraja, Gurminder Kaur, Yatu Rani and Rupam Jha
10.1 Introduction
10.2 AI (Artificial Intelligence)
10.3 Concept of Ethics
10.3.1 Standards of Morality and Integrity for Ethical Implementation of AI
10.3.1.1 Make a Positive Impact on Humanity and Human Welfare while
Understanding that Everyone has an Interest in Computing
10.3.1.2 Avoid Destruction
10.3.1.3 Be Straightforward and Constant
10.3.1.4 Deference to Confidentiality
10.3.1.5 Honour the Effort for Creating Original Concepts, Discoveries, Artistic
Creations, and Technology Products
10.3.1.6 Respect Secrecy
10.3.2 Methods to Resolve Complexities Regarding Ethical Implications
10.3.2.1 Dilemma to Prioritise Code of Morality, Legislation and Supervising Body
10.3.2.2 Dilemma Amongst Moral Principles and Directorial Weight-Age
10.3.2.3 Casual Resolution of Ethical Violations
10.3.2.4 Informing Breach of Ethical Protocol
10.3.2.5 Working in Alliance with Board of Ethics
10.3.2.6 Unacceptable Objections
10.3.2.7 Unreasonable or Being Biased Regarding Petitioners and Defendants
10.4 AI Ethics
10.4.1 Standards Regarding Individual Rights Towards Protection and Human Secrecy in AI-Ethics
10.4.1.1 Integrity & Protection
10.4.1.2 Rights to Confidentiality in Information
10.4.1.3 Rationalisation without Any Damage
10.4.1.4 Investors, Alliance and Coordination with Supervision
10.4.1.5 Fulfilment of Duties and Answerability
10.4.1.6 Precision and Justification
10.4.1.7 Individual or Manual Omission and Presence of Mind
10.4.1.8 Survival with Efficiency
10.4.1.9 Attentiveness and Education
10.4.1.10 Equality and Unbiased
10.4.2 Plan of Action for Ethical Augmentation and Its Execution in AI may Include the Mentioned Policy Framework
10.4.2.1 Ethics First & Responsibility
10.4.2.2 Economical & Employment Aspects
10.4.2.3 Database Regulations
10.4.2.4 Analytical Study and Learning
10.4.2.5 Wellness and Societal Prosperity
10.4.2.6 Non-Discrimination in Males & Females
10.4.2.7 Environmental & Natural Eco-Systems
10.4.3 Two Logical Methods Adopted by UNESCO to Make Certain the Effectiveness in Policies Framed for AI Ethics
10.4.3.1 Framework for Assessing Willingness
10.4.3.2 Analysis of Ethical Outcomes
10.4.4 The Multidimensional Implementation Strategy Includes Such Elements As
10.5 AI Ethics in Business
10.5.1 AI Techniques Implementation Aspects in Various Business Dimensions
10.5.1.1 Refining the Service Quality to End Users
10.5.1.2 Provisioning of Advice in Context to Multiple Products Offered
10.5.1.3 Bifurcating the Target People
10.5.1.4 Analysing the Customer Satisfaction & Contentment for Products Offered
10.5.1.5 Detecting Scam
10.5.1.6 Logistics & Supply Chain Works Seamlessly
10.5.1.7 Hierarchical Model for Analysing AI in Business
10.5.2 Steps to Assure Ethical Application of AI in Business
10.5.2.1 Assessment of Legality & Humanitarian Principles
10.5.2.2 Establishment of New Set of Protocols for Ethical Execution
10.5.2.3 Regulating the Ethical Implications in AI
10.5.2.4 Spreading Awareness amongst Employees
10.5.3 Ethical Execution of AI in Companies - Benefits
10.6 AI Ethics in Medicine
10.6.1 Information Secrecy & Integrity
10.6.2 Answerability and Dependability in Decisiveness with AI Tools
10.6.3 Societal Glitches and Righteousness
10.6.4 Motivation, Emotional Support with Medicinal Discussion
10.6.5 Ways to Improve AI Ethical Dimensions in Medicine
10.6.5.1 Safeguarding Information about Individuals
10.6.5.2 Advance the Public Interest, Security of People, and Wellness
10.6.5.3 Assure Honesty, and Understanding
10.6.5.4 Foster Inclusivity and Equity
10.6.5.5 Advocate for Approachable and Efficient Artificial Intelligence
10.7 AI Ethics in Education
10.8 Conclusion
References
11. Case Study on Soil Identification for Insecticides and Fertilizer Recommendation Using IoT and Deep Learning: An Ethical Approach in Smart Agriculture 4.0
Richa Singh and Rekha Kashyap
11.1 Introduction
11.2 Literature Survey
11.3 Problem Formulation
11.4 Proposed Work
11.5 Result and Discussion
11.6 Conclusion
References
12. Case Study on Ethical AI-Based Decision-Making in E-Commerce Industrial Sector: Insights on McDonald’s and Deliveroo
Anushka Singh, Naman Tyagi and Dolly Sharma
12.1 Introduction
12.2 Foundations of AutoML
12.2.1 Understanding AutoML
12.2.2 Automated Feature Engineering
12.3 Benefits and Challenges
12.3.1 Benefits of AutoML
12.3.1.1 Time Efficiency
12.3.1.2 Democratization of Machine Learning
12.3.1.3 Increased Accessibility
12.3.1.4 Optimized Model Performance
12.3.1.5 Resource Efficiency
12.3.2 Challenges of AutoML
12.3.2.1 Lack of Interpretability
12.3.2.2 Data Quality Dependency
12.3.2.3 Overfitting and Model Selection
12.3.2.4 Algorithmic Bias
12.3.2.5 Complexity and Customization
12.4 Industrial Applications of AutoML: McDonald’s
12.4.1 Background
12.4.2 Introduction
12.4.3 Artificial Intelligence in McDonald’s
12.4.3.1 Drive-Thru Chains
12.4.3.2 Self-Service Kiosk
12.4.3.3 Predictable Purchases
12.4.3.4 Voice Recognition
12.4.4 AutoML Implementation at McDonald’s
12.4.4.1 Operational Streamlining for Unprecedented Efficiency
12.4.4.2 Precision in Marketing Strategies through Personalization
12.4.4.3 Demand Forecasting and Inventory Management
12.4.4.4 Elevating the Customer Experience
12.4.4.5 Adaptability to Local Markets
12.4.4.6 Efficiency Gains and Tangible Cost Reductions
12.4.5 Result and Impact
12.4.5.1 Personalized Marketing Driving Customer Engagement
12.4.5.2 Optimized Drive-Thru Operations for Seamless Experiences
12.4.5.3 Precision in Demand Forecasting
12.4.5.4 Adaptation to Local Markets for Global Success
12.4.5.5 Economic Impact and Cost-Efficiency
12.5 Industrial Applications of AutoML: Deliveroo
12.5.1 Background
12.5.2 Introduction
12.5.3 AWS Tools Used by Deliveroo
12.5.3.1 Amazon Elastic Compute Cloud (EC2)
12.5.3.2 Amazon Simple Storage Service (S3)
12.5.3.3 Amazon Elastic Load Balancing (ELB)
12.5.3.4 Amazon CloudWatch
12.5.3.5 Amazon Route 53
12.5.3.6 AWS Lambda
12.5.3.7 Amazon Simple Queue Service (SQS)
12.5.3.8 Amazon Simple Notification Service (SNS)
12.5.3.9 Amazon DynamoDB
12.5.3.10 AWS CloudFormation
12.5.3.11 Amazon CloudTrail
12.5.3.12 AWS CodePipeline
12.5.3.13 Amazon Kinesis
12.5.4 AWS and AutoML Integration at Deliveroo
12.5.4.1 Scaling Operations with AWS
12.5.4.2 AutoML’s Role in Precision Decision-Making
12.5.4.3 Seamless Data Management and Analytics
12.5.4.4 Dynamic Adaptability to Market Demands
12.5.4.5 Enhancing Customer Experiences
12.5.4.6 Cost-Efficiency and Sustainable Growth
12.5.5 Outcomes and Achievements
12.5.5.1 Exponential Scalability
12.5.5.2 Precision in Delivery Operations
12.5.5.3 Data-Driven Decision-Making
12.5.5.4 Enhanced Customer Experiences
12.5.5.5 Operational Efficiency and Cost Savings
12.5.5.6 Innovation and Competitive Edge
12.6 Ethical Considerations
12.6.1 Data Privacy
12.6.2 Transparency and Explainability
12.6.3 Mitigating Bias and Fostering Fairness
12.6.4 Stakeholder Management and Accountability
12.7 Future Trends
12.7.1 Emerging Trends in AutoML
12.7.1.1 Enhanced Model Explainability
12.7.1.2 Democratization of AI Continues
12.7.1.3 Integration of AutoML with Edge Computing
12.7.1.4 Hybrid Cloud Deployments for Flexibility
12.7.1.5 AutoML for Structured and Unstructured Data
12.7.1.6 Integration of AutoML in Industry-Specific Solutions
12.7.1.7 Continuous Model Monitoring and Maintenance
12.7.1.8 Emphasis on Responsible AI and Ethical Considerations
12.7.1.9 Quantum Computing’s Impact on AutoML
12.7.2 Considerations for Implementation
12.7.2.1 Clearly Defined Objectives
12.7.2.2 Data Quality and Accessibility
12.7.2.3 Skillset and Training
12.7.2.4 Regulatory Compliance and Ethical Considerations
12.7.2.5 Data Security and Privacy
12.7.2.6 Integration with Existing Systems
12.7.2.7 Cost Considerations and ROI
12.7.2.8 Vendor Selection and Partnerships
12.7.2.9 Scalability and Futureproofing
12.7.2.10 Change Management and User Adoption
12.7.2.11 Continuous Monitoring and Optimization
12.8 Conclusion
References
13. AI Insights: Navigating Education News Ethically Through Aggregation and Sentiment Analysis
Anshumaan Garg and Dolly Sharma
13.1 Introduction
13.1.1 Basics of Sentiment Analysis
13.1.2 Data Scraping from Web
13.1.3 Vader
13.1.4 BeautifulSoup
13.1.5 Sentiment Analysis
13.1.6 Web Scraping
13.1.7 Scope
13.1.8 Objectives
13.1.9 Chapter Outline
13.2 Literature Review
13.2.1 Working of BeautifulSoup
13.2.2 API-Based Data Extraction
13.2.3 Natural Language Tool-Kit
13.2.4 Preprocessing
13.2.5 Working of Vader
13.3 Methodology
13.3.1 Creation of a Virtual Environment
13.3.2 Installation of Python Libraries
13.3.3 Code Editor Used for Programming
13.3.4 Commands Used
13.3.5 Django MVT Architecture
13.3.6 Modules and Functions Used
13.3.7 Working
13.3.7.1 News Aggregation
13.3.8 Sentiment Analysis
13.3.8.1 API User Verification
13.4 Results Discussion
13.4.1 View of Website
13.4.2 News Aggregator
13.4.3 Sentiment Analysis
13.4.4 Advantages
13.4.5 Disadvantages
13.5 Conclusion and Future Work
13.5.1 Conclusion
13.5.2 Future Work
References
14. Case Study on AI-Based Ethical Decision-Making for Smart Transportation
S. Muthu Lakshmi, K. Mythili, Malathy Sathyamoorthy, Rajesh Kumar Dhanaraj and Aanjan Kumar S.
14.1 Introduction
14.2 Artificial Intelligence
14.3 Role of Artificial Intelligence in Transportation
14.4 Literature Review
14.4.1 Autonomous Vehicle
14.4.2 Communication Between Vehicles
14.4.3 Tracking Using GPS
14.5 Challenges
14.6 AI Ethics
14.6.1 AI Smart Transportation Use Cases
14.6.1.1 Object Detection
14.6.1.2 Driver Monitoring
14.6.1.3 Route Prediction
14.6.1.4 Smart Traffic Lights
14.6.2 Ethics in Autonomous Vehicles
14.6.3 Managing Traffic and Congestion Prediction
14.6.4 Decision-Making Process in Smart Transportation Systems
14.7 Data Confidentiality and Security
14.8 Vision from Data: Smart Decision-Making in Transportation
14.9 Conclusions
14.10 Future Directions
References
15. Case Study on AI-Based Decision-Making in E-Commerce: Exploring Location-Based Insights for Analysis of Geospatial Data
Ashima Arya, Daksh Rampal, Ekagra, Kashish Varshney, Rohit Vashisht and Yonis Gulzar
15.1 Introduction
15.1.1 Method of Geospatial Data Analysis
15.2 Objective
15.3 Background Knowledge
15.4 Related Work
15.5 Data Analysis of Geolocation Data
15.6 Proposed Methodology
15.7 Results
15.8 Conclusion
15.9 Future
Acknowledgment
References
Index
Back to Top
Preface
1. Standards, Policies, Ethical Guidelines and Governance in Artificial Intelligence: Insights on the Financial Sector
Purohit S. and Arora, R.
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
2. Domain-Specific AI Algorithms and Models in Decision-Making: An Overview
P. Kanaga Priya, A. Reethika, Malathy Sathyamoorthy and Rajesh Kumar Dhanaraj
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 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
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
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
3. Role of AI in Decision-Making – A Comprehensive Study
Rohit Vashisht, Sonia Deshmukh and Ashima Arya
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
4. Ethical Challenges in AI Decision‑Making: From the User’s Perspective
M. Nalini, S. Sandhya and S. Shiwani
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
5. Ethical Decision-Making in Yoga Posture Detection through AI: Fostering Responsible Technology Integration
Ishita Jain, Riya Srivastava, Vanshita Srivastava, Vanshika Sinha and Abhinav Juneja
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
5.1.3 Ethical Decision-Making in Yoga Posture Detection through AI
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
6. Ethical AI: A Design of an Integrated Framework towards Intelligent Decision-Making in Stock Control
Mini Verma and Palak Gupta
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
7. Integrating Machine Learning and Data Ethics: Frameworks for Intelligent Ethical Decision-Making
Karishma Sharma, Deepa Gupta, Mukul Gupta and Rajesh Dhanaraj
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
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
8. Importance of Human Loop in AI-Based Decision-Making: Strengthening the Ethical Perspective
A. Reethika, P. Kanaga Priya, Malathy Sathyamoorthy and Rajesh Kumar Dhanaraj
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
9. AI in Finance and Business: Novel Method for Human Resource Recommendation Using Improved Gradient Boosting Tree Model
Mahima Shanker Pandey, Abhishek Singh, Bihari Nandan Pandey, Aparna Sharma
and Prashant Upadhyay
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
10. Comprehensive View from Ethics to AI Ethics: With Multifaceted Dimensions
Kanika Budhiraja, Gurminder Kaur, Yatu Rani and Rupam Jha
10.1 Introduction
10.2 AI (Artificial Intelligence)
10.3 Concept of Ethics
10.3.1 Standards of Morality and Integrity for Ethical Implementation of AI
10.3.1.1 Make a Positive Impact on Humanity and Human Welfare while
Understanding that Everyone has an Interest in Computing
10.3.1.2 Avoid Destruction
10.3.1.3 Be Straightforward and Constant
10.3.1.4 Deference to Confidentiality
10.3.1.5 Honour the Effort for Creating Original Concepts, Discoveries, Artistic
Creations, and Technology Products
10.3.1.6 Respect Secrecy
10.3.2 Methods to Resolve Complexities Regarding Ethical Implications
10.3.2.1 Dilemma to Prioritise Code of Morality, Legislation and Supervising Body
10.3.2.2 Dilemma Amongst Moral Principles and Directorial Weight-Age
10.3.2.3 Casual Resolution of Ethical Violations
10.3.2.4 Informing Breach of Ethical Protocol
10.3.2.5 Working in Alliance with Board of Ethics
10.3.2.6 Unacceptable Objections
10.3.2.7 Unreasonable or Being Biased Regarding Petitioners and Defendants
10.4 AI Ethics
10.4.1 Standards Regarding Individual Rights Towards Protection and Human Secrecy in AI-Ethics
10.4.1.1 Integrity & Protection
10.4.1.2 Rights to Confidentiality in Information
10.4.1.3 Rationalisation without Any Damage
10.4.1.4 Investors, Alliance and Coordination with Supervision
10.4.1.5 Fulfilment of Duties and Answerability
10.4.1.6 Precision and Justification
10.4.1.7 Individual or Manual Omission and Presence of Mind
10.4.1.8 Survival with Efficiency
10.4.1.9 Attentiveness and Education
10.4.1.10 Equality and Unbiased
10.4.2 Plan of Action for Ethical Augmentation and Its Execution in AI may Include the Mentioned Policy Framework
10.4.2.1 Ethics First & Responsibility
10.4.2.2 Economical & Employment Aspects
10.4.2.3 Database Regulations
10.4.2.4 Analytical Study and Learning
10.4.2.5 Wellness and Societal Prosperity
10.4.2.6 Non-Discrimination in Males & Females
10.4.2.7 Environmental & Natural Eco-Systems
10.4.3 Two Logical Methods Adopted by UNESCO to Make Certain the Effectiveness in Policies Framed for AI Ethics
10.4.3.1 Framework for Assessing Willingness
10.4.3.2 Analysis of Ethical Outcomes
10.4.4 The Multidimensional Implementation Strategy Includes Such Elements As
10.5 AI Ethics in Business
10.5.1 AI Techniques Implementation Aspects in Various Business Dimensions
10.5.1.1 Refining the Service Quality to End Users
10.5.1.2 Provisioning of Advice in Context to Multiple Products Offered
10.5.1.3 Bifurcating the Target People
10.5.1.4 Analysing the Customer Satisfaction & Contentment for Products Offered
10.5.1.5 Detecting Scam
10.5.1.6 Logistics & Supply Chain Works Seamlessly
10.5.1.7 Hierarchical Model for Analysing AI in Business
10.5.2 Steps to Assure Ethical Application of AI in Business
10.5.2.1 Assessment of Legality & Humanitarian Principles
10.5.2.2 Establishment of New Set of Protocols for Ethical Execution
10.5.2.3 Regulating the Ethical Implications in AI
10.5.2.4 Spreading Awareness amongst Employees
10.5.3 Ethical Execution of AI in Companies - Benefits
10.6 AI Ethics in Medicine
10.6.1 Information Secrecy & Integrity
10.6.2 Answerability and Dependability in Decisiveness with AI Tools
10.6.3 Societal Glitches and Righteousness
10.6.4 Motivation, Emotional Support with Medicinal Discussion
10.6.5 Ways to Improve AI Ethical Dimensions in Medicine
10.6.5.1 Safeguarding Information about Individuals
10.6.5.2 Advance the Public Interest, Security of People, and Wellness
10.6.5.3 Assure Honesty, and Understanding
10.6.5.4 Foster Inclusivity and Equity
10.6.5.5 Advocate for Approachable and Efficient Artificial Intelligence
10.7 AI Ethics in Education
10.8 Conclusion
References
11. Case Study on Soil Identification for Insecticides and Fertilizer Recommendation Using IoT and Deep Learning: An Ethical Approach in Smart Agriculture 4.0
Richa Singh and Rekha Kashyap
11.1 Introduction
11.2 Literature Survey
11.3 Problem Formulation
11.4 Proposed Work
11.5 Result and Discussion
11.6 Conclusion
References
12. Case Study on Ethical AI-Based Decision-Making in E-Commerce Industrial Sector: Insights on McDonald’s and Deliveroo
Anushka Singh, Naman Tyagi and Dolly Sharma
12.1 Introduction
12.2 Foundations of AutoML
12.2.1 Understanding AutoML
12.2.2 Automated Feature Engineering
12.3 Benefits and Challenges
12.3.1 Benefits of AutoML
12.3.1.1 Time Efficiency
12.3.1.2 Democratization of Machine Learning
12.3.1.3 Increased Accessibility
12.3.1.4 Optimized Model Performance
12.3.1.5 Resource Efficiency
12.3.2 Challenges of AutoML
12.3.2.1 Lack of Interpretability
12.3.2.2 Data Quality Dependency
12.3.2.3 Overfitting and Model Selection
12.3.2.4 Algorithmic Bias
12.3.2.5 Complexity and Customization
12.4 Industrial Applications of AutoML: McDonald’s
12.4.1 Background
12.4.2 Introduction
12.4.3 Artificial Intelligence in McDonald’s
12.4.3.1 Drive-Thru Chains
12.4.3.2 Self-Service Kiosk
12.4.3.3 Predictable Purchases
12.4.3.4 Voice Recognition
12.4.4 AutoML Implementation at McDonald’s
12.4.4.1 Operational Streamlining for Unprecedented Efficiency
12.4.4.2 Precision in Marketing Strategies through Personalization
12.4.4.3 Demand Forecasting and Inventory Management
12.4.4.4 Elevating the Customer Experience
12.4.4.5 Adaptability to Local Markets
12.4.4.6 Efficiency Gains and Tangible Cost Reductions
12.4.5 Result and Impact
12.4.5.1 Personalized Marketing Driving Customer Engagement
12.4.5.2 Optimized Drive-Thru Operations for Seamless Experiences
12.4.5.3 Precision in Demand Forecasting
12.4.5.4 Adaptation to Local Markets for Global Success
12.4.5.5 Economic Impact and Cost-Efficiency
12.5 Industrial Applications of AutoML: Deliveroo
12.5.1 Background
12.5.2 Introduction
12.5.3 AWS Tools Used by Deliveroo
12.5.3.1 Amazon Elastic Compute Cloud (EC2)
12.5.3.2 Amazon Simple Storage Service (S3)
12.5.3.3 Amazon Elastic Load Balancing (ELB)
12.5.3.4 Amazon CloudWatch
12.5.3.5 Amazon Route 53
12.5.3.6 AWS Lambda
12.5.3.7 Amazon Simple Queue Service (SQS)
12.5.3.8 Amazon Simple Notification Service (SNS)
12.5.3.9 Amazon DynamoDB
12.5.3.10 AWS CloudFormation
12.5.3.11 Amazon CloudTrail
12.5.3.12 AWS CodePipeline
12.5.3.13 Amazon Kinesis
12.5.4 AWS and AutoML Integration at Deliveroo
12.5.4.1 Scaling Operations with AWS
12.5.4.2 AutoML’s Role in Precision Decision-Making
12.5.4.3 Seamless Data Management and Analytics
12.5.4.4 Dynamic Adaptability to Market Demands
12.5.4.5 Enhancing Customer Experiences
12.5.4.6 Cost-Efficiency and Sustainable Growth
12.5.5 Outcomes and Achievements
12.5.5.1 Exponential Scalability
12.5.5.2 Precision in Delivery Operations
12.5.5.3 Data-Driven Decision-Making
12.5.5.4 Enhanced Customer Experiences
12.5.5.5 Operational Efficiency and Cost Savings
12.5.5.6 Innovation and Competitive Edge
12.6 Ethical Considerations
12.6.1 Data Privacy
12.6.2 Transparency and Explainability
12.6.3 Mitigating Bias and Fostering Fairness
12.6.4 Stakeholder Management and Accountability
12.7 Future Trends
12.7.1 Emerging Trends in AutoML
12.7.1.1 Enhanced Model Explainability
12.7.1.2 Democratization of AI Continues
12.7.1.3 Integration of AutoML with Edge Computing
12.7.1.4 Hybrid Cloud Deployments for Flexibility
12.7.1.5 AutoML for Structured and Unstructured Data
12.7.1.6 Integration of AutoML in Industry-Specific Solutions
12.7.1.7 Continuous Model Monitoring and Maintenance
12.7.1.8 Emphasis on Responsible AI and Ethical Considerations
12.7.1.9 Quantum Computing’s Impact on AutoML
12.7.2 Considerations for Implementation
12.7.2.1 Clearly Defined Objectives
12.7.2.2 Data Quality and Accessibility
12.7.2.3 Skillset and Training
12.7.2.4 Regulatory Compliance and Ethical Considerations
12.7.2.5 Data Security and Privacy
12.7.2.6 Integration with Existing Systems
12.7.2.7 Cost Considerations and ROI
12.7.2.8 Vendor Selection and Partnerships
12.7.2.9 Scalability and Futureproofing
12.7.2.10 Change Management and User Adoption
12.7.2.11 Continuous Monitoring and Optimization
12.8 Conclusion
References
13. AI Insights: Navigating Education News Ethically Through Aggregation and Sentiment Analysis
Anshumaan Garg and Dolly Sharma
13.1 Introduction
13.1.1 Basics of Sentiment Analysis
13.1.2 Data Scraping from Web
13.1.3 Vader
13.1.4 BeautifulSoup
13.1.5 Sentiment Analysis
13.1.6 Web Scraping
13.1.7 Scope
13.1.8 Objectives
13.1.9 Chapter Outline
13.2 Literature Review
13.2.1 Working of BeautifulSoup
13.2.2 API-Based Data Extraction
13.2.3 Natural Language Tool-Kit
13.2.4 Preprocessing
13.2.5 Working of Vader
13.3 Methodology
13.3.1 Creation of a Virtual Environment
13.3.2 Installation of Python Libraries
13.3.3 Code Editor Used for Programming
13.3.4 Commands Used
13.3.5 Django MVT Architecture
13.3.6 Modules and Functions Used
13.3.7 Working
13.3.7.1 News Aggregation
13.3.8 Sentiment Analysis
13.3.8.1 API User Verification
13.4 Results Discussion
13.4.1 View of Website
13.4.2 News Aggregator
13.4.3 Sentiment Analysis
13.4.4 Advantages
13.4.5 Disadvantages
13.5 Conclusion and Future Work
13.5.1 Conclusion
13.5.2 Future Work
References
14. Case Study on AI-Based Ethical Decision-Making for Smart Transportation
S. Muthu Lakshmi, K. Mythili, Malathy Sathyamoorthy, Rajesh Kumar Dhanaraj and Aanjan Kumar S.
14.1 Introduction
14.2 Artificial Intelligence
14.3 Role of Artificial Intelligence in Transportation
14.4 Literature Review
14.4.1 Autonomous Vehicle
14.4.2 Communication Between Vehicles
14.4.3 Tracking Using GPS
14.5 Challenges
14.6 AI Ethics
14.6.1 AI Smart Transportation Use Cases
14.6.1.1 Object Detection
14.6.1.2 Driver Monitoring
14.6.1.3 Route Prediction
14.6.1.4 Smart Traffic Lights
14.6.2 Ethics in Autonomous Vehicles
14.6.3 Managing Traffic and Congestion Prediction
14.6.4 Decision-Making Process in Smart Transportation Systems
14.7 Data Confidentiality and Security
14.8 Vision from Data: Smart Decision-Making in Transportation
14.9 Conclusions
14.10 Future Directions
References
15. Case Study on AI-Based Decision-Making in E-Commerce: Exploring Location-Based Insights for Analysis of Geospatial Data
Ashima Arya, Daksh Rampal, Ekagra, Kashish Varshney, Rohit Vashisht and Yonis Gulzar
15.1 Introduction
15.1.1 Method of Geospatial Data Analysis
15.2 Objective
15.3 Background Knowledge
15.4 Related Work
15.5 Data Analysis of Geolocation Data
15.6 Proposed Methodology
15.7 Results
15.8 Conclusion
15.9 Future
Acknowledgment
References
Index
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