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 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.
Table of Contents
Preface xxi
1 Standards, Policies, Ethical Guidelines and Governance in Artificial Intelligence: Insights on the Financial Sector 1
Purohit S. and Arora, R.
1.1 Introduction 2
1.2 Chatbots in the Financial Industry 3
1.3 Background of the Study 5
1.4 Literature Review 6
1.5 Understanding Bias in Customer Service Chatbots 8
1.5.1 Categorizing Biases in Financial Chatbots 8
1.5.2 Sources and Origins of Bias in Financial Chatbots 9
1.5.3 User Feedback and Bias Detection 9
1.5.4 The Role of Explainability in Unveiling Bias 9
1.6 Impact of Bias in Financial Chatbot Interactions 10
1.6.1 Customer Trust and Satisfaction 10
1.6.2 Perpetuation of Inequalities 10
1.6.3 Reputational Risks for Financial Institutions 10
1.6.4 Regulatory Compliance Challenges 10
1.6.5 Implications for Brand Image 11
1.7 Strategies for Mitigating Bias in Financial Customer Service Chatbots 11
1.7.1 Diverse and Representative Training Data 12
1.7.2 Continuous Monitoring and Iterative Improvement 12
1.7.3 Explainability Features for User Trust 12
1.7.4 Inclusive User Testing 12
1.7.5 Ethical Guidelines and Governance 13
1.7.6 Collaborative Partnerships with Ethical AI Experts 13
1.8 Ethical Considerations and Transparency in Financial Chatbot Firms 13
1.9 Future Directions and Recommendations 15
1.10 Conclusion 16
References 16
2 Domain-Specific AI Algorithms and Models in Decision-Making: An Overview 27
P. Kanaga Priya, A. Reethika, Malathy Sathyamoorthy and Rajesh Kumar Dhanaraj
2.1 Introduction 28
2.1.1 Overview of the Role of AI in Decision Making 28
2.1.1.1 The Emergence of Artificial Intelligence: How it is Changing Decision-Making in Several Domains of Economics 29
2.1.1.2 Putting the Power of Artificial Intelligence to Work in a Particular Field 29
2.1.1.3 The AI-Assisted Decision-Making Process 29
2.1.1.4 Benefits and Future of AI-Powered Decision-Making 31
2.1.2 Importance of Domain-Specific Approaches 32
2.1.2.1 Advantages of Domain-Specific AI 33
2.1.2.2 Instances of Domain-Specific AI in Action 33
2.1.2.3 General AI versus Domain-Specific AI: Powering Intelligent Decisions 34
2.2 Understanding Domain-Specific Decision Making 36
2.2.1 Bridging the Gap: Explainable AI for Effective Collaboration between Machine Learning and Domain Expertise 37
2.3 Building Blocks of AI for Decision-Making 38
2.3.1 Overview of AI Approaches 38
2.3.2 Machine Learning for Data-Driven Decision Generating 38
2.3.3 Knowledge-Based Systems for Rule-Based Decision-Making 39
2.3.4 Reinforcement Learning in Dynamic Environments 39
2.4 Domain-Specific AI: Revolutionizing Industries 39
2.4.1 Healthcare 40
2.4.1.1 The Importance of Patient-Centered Design in Regulating Large Language Models or Generative AI 40
2.4.1.2 XAI in Biomedicine: A Post-Pandemic Surge for Trustworthy AI in Healthcare Delivery 41
2.4.2 Finance 41
2.4.2.1 Explainable AI: A Path Toward Trustworthy and Ethical Applications of Machine Learning in Finance 42
2.4.2.2 Learning Machines, Evolving Markets: The Need for Adaptable Generative AI in Finance 42
2.4.3 Manufacturing 43
2.4.3.1 The Rise of Generative AI: A Call for Responsible AI Frameworks in MSME Manufacturing 44
2.4.3.2 Guiding the Future of Manufacturing: Responsible AI as a Cornerstone for Sustainable and Ethical Production 44
2.4.4 Transportation 44
2.4.4.1 Revolutionizing Urban Mobility: The Power of Machine Learning and AI in Smart City Transportation 45
2.4.4.2 AI Revolutionizes Transportation: Boosting Efficiency, Safety, and New Business Opportunities 45
2.4.5 Agriculture 46
2.4.5.1 Cultivating a Sustainable Future: How AI and Big Data are Revolutionizing Precision Agriculture 47
2.4.5.2 AI in the Fields: From Precision Irrigation to Smart Robots, How Artificial Intelligence Is Revolutionizing Agribusiness 47
2.4.6 Retail 48
2.4.6.1 The Generative Retail Revolution: How AI is Personalizing Customer Experience, Optimizing Inventory, and Driving Sales 48
2.4.6.2 The Future of Retail: Leveraging AI for Efficiency and Personalization while Navigating Data Privacy and Ethical Challenges 49
2.4.7 Domain-Specific AI: A Comparative Analysis 49
2.5 Ethical and Societal Implications 51
2.6 Future Directions and Emerging Trends 51
2.7 Conclusion 52
References 52
3 Role of AI in Decision-Making – A Comprehensive Study 55
Rohit Vashisht, Sonia Deshmukh and Ashima Arya
3.1 Introduction 56
3.2 Need of AI-Based Decision-Making System 58
3.3 Major Obstacle for AI-Based Decision-Making System 62
3.4 Applications of AI-Based Decision-Making System 65
3.5 Case Study: AIDMS for Age-Related Macular Degeneration (amd) 70
3.6 Conclusion and Future Directions 75
References 76
4 Ethical Challenges in AI Decision‐Making: From the User’s Perspective 79
M. Nalini, S. Sandhya and S. Shiwani
4.1 Introduction 80
4.1.1 Ethical Principles in AI 81
4.1.2 The Role of Data in AI Decision-Making 82
4.2 Public Perception towards AI 85
4.3 Ethical Dilemmas of AI 87
4.4 Emerging Issues that are Prevailing in the Current World 90
4.4.1 Case Studies 91
4.4.2 Collaboration and Stakeholder Involvement 92
4.5 Future Considerations 95
4.5.1 Conclusion 95
References 96
5 Ethical Decision-Making in Yoga Posture Detection through AI: Fostering Responsible Technology Integration 99
Ishita Jain, Riya Srivastava, Vanshita Srivastava, Vanshika Sinha and Abhinav Juneja
5.1 Introduction 100
5.1.1 About Yoga 103
5.1.1.1 Advantages and Disadvantages of Yoga 104
5.1.2 Posture Detection System 106
5.1.2.1 Components of Posture Detection System 107
5.1.2.2 Process of Posture Detection System 107
5.1.2.3 Applications of Posture Detection System 108
5.1.2.4 Advantages and Disadvantages of Posture Detection System 108
5.1.3 Ethical Decision-Making in Yoga Posture Detection through AI 110
5.2 Literature Review 111
5.3 Technologies Used 112
5.3.1 MediaPipe 112
5.3.2 OpenCV (Open-Source Computer Vision Library) 113
5.4 Dataset Used 115
5.5 Methodology 117
5.5.1 How Does It Work? 118
5.6 Conclusion 119
References 121
6 Ethical AI: A Design of an Integrated Framework towards Intelligent Decision-Making in Stock Control 125
Mini Verma and Palak Gupta
6.1 Introduction 126
6.1.1 The Effect of Artificial Intelligence on Controlling Inventory 126
6.1.2 Process of Evolution and Development in Stock Control 127
6.2 Benefits and Impact of AI on Inventory Control 128
6.2.1 Moral Considerations in AI-Primarily Based Selection Making 130
6.3 Best Practices for Implementing AI for Stock Management in E-Commerce 131
6.3.1 Consideration in Statistics and Statistics Safety 131
6.3.2 How AI Enables Stock Administration for Important Corporations 132
6.3.3 Synthetic Intelligence in Inventory Administration: Destiny Styles and Extension 134
6.3.4 Inventory Control with Predictive Renovation 135
6.4 Formulation of Proposed Model 138
6.4.1 Framework Discussion 139
6.4.2 Assumptions and Notations 140
6.4.3 Proposed Mathematical Model 140
6.4.4 Example 146
6.4.5 Sensitivity Analysis 146
6.5 Conclusion 148
References 150
7 Integrating Machine Learning and Data Ethics: Frameworks for Intelligent Ethical Decision-Making 153
Karishma Sharma, Deepa Gupta, Mukul Gupta and Rajesh Dhanaraj
7.1 Introduction 154
7.2 Concept of Machine Learning and Data Ethics 155
7.3 Importance of ML and AI in Design Making 157
7.4 Defining an Intelligent Decision-Making Support System 158
7.5 Transformation of the Decision-Making System to Intelligent Decision-Making Support 159
7.6 Architecture Framework 161
7.6.1 Components of the IDSS Architecture 161
7.7 Conceptual Framework 162
7.7.1 Core Concepts 162
7.7.2 Components of the Conceptual Framework 163
7.7.3 Block Diagram of the Conceptual Framework 164
7.7.4 Principles of Framework 165
7.7.4.1 Tools Used in IDMSS 168
7.7.4.2 Data Processing Tools 168
7.7.4.3 Machine Learning Frameworks 168
7.7.4.4 Cloud Computing Platforms 168
7.7.5 Analyzing Different Tools 169
7.7.6 Data Processing Tools 169
7.7.7 Machine Learning Frameworks 169
7.7.8 Convolutional Neural Networks (CNNs) 169
7.7.9 Recurrent Neural Networks (RNNs) 170
7.7.10 Cloud Computing Platforms 170
7.8 Cloud-Based Scalability with Auto Scaling 170
7.9 Case Study of Complex Problem Using Framework 174
7.10 Algorithm and Coding Analysis 174
7.11 Results and Impact Analysis 178
7.12 Conclusion 178
References 179
8 Importance of Human Loop in AI-Based Decision-Making: Strengthening the Ethical Perspective 183
A. Reethika, P. Kanaga Priya, Malathy Sathyamoorthy and Rajesh Kumar Dhanaraj
8.1 Introduction 184
8.1.1 Human-in-the-Loop 185
8.2 Human Interaction with AI Platform 186
8.3 Human and Machine Ethical Annotation 187
8.4 Exploring AI with Human-in-the-Loop Technique 191
8.4.1 AI-Ethical Module 193
8.4.2 Role of HITL in Ethical Decision-Making 193
8.5 Creating Ethical AI Using HTIL Technique 195
8.5.1 Distributed Ethical Decision System 197
8.5.2 Viability and Advantages of Decision-Making Using Ethical AI 198
8.5.3 Problem Statement 200
8.6 Conclusion 203
References 204
9 AI in Finance and Business: Novel Method for Human Resource Recommendation Using Improved Gradient Boosting Tree Model 207
Mahima Shanker Pandey, Abhishek Singh, Bihari Nandan Pandey, Aparna Sharma and Prashant Upadhyay
9.1 Introduction 208
9.2 Literature Review 210
9.2.1 Deep Learning Approach 210
9.2.2 Gradient Boosting Tree 212
9.2.3 Convolutional Neural Network 214
9.2.3.1 Layer of Convolution 214
9.2.3.2 Pool Layer 215
9.2.3.3 Active Layer 216
9.2.3.4 Full Connection Layer 216
9.2.4 Deeper Learning Organizational Techniques 216
9.3 The Proposed Model 217
9.4 Evaluation of the Impact of the Technology 218
9.4.1 Data Set 218
9.4.1.1 Evaluation Criteria 219
9.5 Conclusion 222
References 222
10 Comprehensive View from Ethics to AI Ethics: With Multifaceted Dimensions 227
Kanika Budhiraja, Gurminder Kaur, Yatu Rani and Rupam Jha
10.1 Introduction 228
10.2 AI (Artificial Intelligence) 230
10.3 Concept of Ethics 234
10.3.1 Standards of Morality and Integrity for Ethical Implementation of AI 236
10.3.1.1 Make a Positive Impact on Humanity and Human Welfare while Understanding that Everyone has an Interest in Computing 236
10.3.1.2 Avoid Destruction 236
10.3.1.3 Be Straightforward and Constant 237
10.3.1.4 Deference to Confidentiality 237
10.3.1.5 Honour the Effort for Creating Original Concepts, Discoveries, Artistic Creations, and Technology Products 237
10.3.1.6 Respect Secrecy 237
10.3.2 Methods to Resolve Complexities Regarding Ethical Implications 238
10.3.2.1 Dilemma to Prioritise Code of Morality, Legislation and Supervising Body 238
10.3.2.2 Dilemma Amongst Moral Principles and Directorial Weight-Age 238
10.3.2.3 Casual Resolution of Ethical Violations 238
10.3.2.4 Informing Breach of Ethical Protocol 238
10.3.2.5 Working in Alliance with Board of Ethics 239
10.3.2.6 Unacceptable Objections 239
10.3.2.7 Unreasonable or Being Biased Regarding Petitioners and Defendants 239
10.4 AI Ethics 239
10.4.1 Standards Regarding Individual Rights Towards Protection and Human Secrecy in AI-Ethics 240
10.4.1.1 Integrity & Protection 240
10.4.1.2 Rights to Confidentiality in Information 240
10.4.1.3 Rationalisation without Any Damage 240
10.4.1.4 Investors, Alliance and Coordination with Supervision 241
10.4.1.5 Fulfilment of Duties and Answerability 241
10.4.1.6 Precision and Justification 241
10.4.1.7 Individual or Manual Omission and Presence of Mind 241
10.4.1.8 Survival with Efficiency 241
10.4.1.9 Attentiveness and Education 241
10.4.1.10 Equality and Unbiased 242
10.4.2 Plan of Action for Ethical Augmentation and Its Execution in AI may Include the Mentioned Policy Framework 242
10.4.2.1 Ethics First & Responsibility 242
10.4.2.2 Economical & Employment Aspects 242
10.4.2.3 Database Regulations 242
10.4.2.4 Analytical Study and Learning 243
10.4.2.5 Wellness and Societal Prosperity 243
10.4.2.6 Non-Discrimination in Males & Females 243
10.4.2.7 Environmental & Natural Eco-Systems 243
10.4.3 Two Logical Methods Adopted by UNESCO to Make Certain the Effectiveness in Policies Framed for AI Ethics 244
10.4.3.1 Framework for Assessing Willingness 244
10.4.3.2 Analysis of Ethical Outcomes 244
10.4.4 The Multidimensional Implementation Strategy Includes Such Elements As 245
10.5 AI Ethics in Business 245
10.5.1 AI Techniques Implementation Aspects in Various Business Dimensions 246
10.5.1.1 Refining the Service Quality to End Users 246
10.5.1.2 Provisioning of Advice in Context to Multiple Products Offered 246
10.5.1.3 Bifurcating the Target People 246
10.5.1.4 Analysing the Customer Satisfaction & Contentment for Products Offered 246
10.5.1.5 Detecting Scam 247
10.5.1.6 Logistics & Supply Chain Works Seamlessly 247
10.5.1.7 Hierarchical Model for Analysing AI in Business 247
10.5.2 Steps to Assure Ethical Application of AI in Business 248
10.5.2.1 Assessment of Legality & Humanitarian Principles 249
10.5.2.2 Establishment of New Set of Protocols for Ethical Execution 249
10.5.2.3 Regulating the Ethical Implications in AI 249
10.5.2.4 Spreading Awareness amongst Employees 249
10.5.3 Ethical Execution of AI in Companies - Benefits 250
10.6 AI Ethics in Medicine 250
10.6.1 Information Secrecy & Integrity 251
10.6.2 Answerability and Dependability in Decisiveness with AI Tools 251
10.6.3 Societal Glitches and Righteousness 251
10.6.4 Motivation, Emotional Support with Medicinal Discussion 252
10.6.5 Ways to Improve AI Ethical Dimensions in Medicine 252
10.6.5.1 Safeguarding Information about Individuals 252
10.6.5.2 Advance the Public Interest, Security of People, and Wellness 252
10.6.5.3 Assure Honesty, and Understanding 253
10.6.5.4 Foster Inclusivity and Equity 253
10.6.5.5 Advocate for Approachable and Efficient Artificial Intelligence 253
10.7 AI Ethics in Education 254
10.8 Conclusion 255
References 256
11 Case Study on Soil Identification for Insecticides and Fertilizer Recommendation Using IoT and Deep Learning: An Ethical Approach in Smart Agriculture 4.0 259
Richa Singh and Rekha Kashyap
11.1 Introduction 260
11.2 Literature Survey 264
11.3 Problem Formulation 268
11.4 Proposed Work 269
11.5 Result and Discussion 271
11.6 Conclusion 276
References 277
12 Case Study on Ethical AI-Based Decision-Making in E-Commerce Industrial Sector: Insights on McDonald’s and Deliveroo 283
Anushka Singh, Naman Tyagi and Dolly Sharma
12.1 Introduction 284
12.2 Foundations of AutoML 284
12.2.1 Understanding AutoML 285
12.2.2 Automated Feature Engineering 285
12.3 Benefits and Challenges 286
12.3.1 Benefits of AutoML 286
12.3.1.1 Time Efficiency 286
12.3.1.2 Democratization of Machine Learning 286
12.3.1.3 Increased Accessibility 287
12.3.1.4 Optimized Model Performance 287
12.3.1.5 Resource Efficiency 287
12.3.2 Challenges of AutoML 287
12.3.2.1 Lack of Interpretability 287
12.3.2.2 Data Quality Dependency 288
12.3.2.3 Overfitting and Model Selection 288
12.3.2.4 Algorithmic Bias 288
12.3.2.5 Complexity and Customization 288
12.4 Industrial Applications of AutoML: McDonald’s 289
12.4.1 Background 289
12.4.2 Introduction 289
12.4.3 Artificial Intelligence in McDonald’s 290
12.4.3.1 Drive-Thru Chains 290
12.4.3.2 Self-Service Kiosk 291
12.4.3.3 Predictable Purchases 291
12.4.3.4 Voice Recognition 292
12.4.4 AutoML Implementation at McDonald’s 293
12.4.4.1 Operational Streamlining for Unprecedented Efficiency 294
12.4.4.2 Precision in Marketing Strategies through Personalization 294
12.4.4.3 Demand Forecasting and Inventory Management 294
12.4.4.4 Elevating the Customer Experience 294
12.4.4.5 Adaptability to Local Markets 294
12.4.4.6 Efficiency Gains and Tangible Cost Reductions 294
12.4.5 Result and Impact 294
12.4.5.1 Personalized Marketing Driving Customer Engagement 294
12.4.5.2 Optimized Drive-Thru Operations for Seamless Experiences 295
12.4.5.3 Precision in Demand Forecasting 295
12.4.5.4 Adaptation to Local Markets for Global Success 295
12.4.5.5 Economic Impact and Cost-Efficiency 295
12.5 Industrial Applications of AutoML: Deliveroo 295
12.5.1 Background 295
12.5.2 Introduction 296
12.5.3 AWS Tools Used by Deliveroo 297
12.5.3.1 Amazon Elastic Compute Cloud (EC2) 298
12.5.3.2 Amazon Simple Storage Service (S3) 298
12.5.3.3 Amazon Elastic Load Balancing (ELB) 298
12.5.3.4 Amazon CloudWatch 299
12.5.3.5 Amazon Route 53 299
12.5.3.6 AWS Lambda 299
12.5.3.7 Amazon Simple Queue Service (SQS) 299
12.5.3.8 Amazon Simple Notification Service (SNS) 299
12.5.3.9 Amazon DynamoDB 299
12.5.3.10 AWS CloudFormation 300
12.5.3.11 Amazon CloudTrail 300
12.5.3.12 AWS CodePipeline 300
12.5.3.13 Amazon Kinesis 300
12.5.4 AWS and AutoML Integration at Deliveroo 300
12.5.4.1 Scaling Operations with AWS 301
12.5.4.2 AutoML’s Role in Precision Decision- Making 301
12.5.4.3 Seamless Data Management and Analytics 301
12.5.4.4 Dynamic Adaptability to Market Demands 301
12.5.4.5 Enhancing Customer Experiences 301
12.5.4.6 Cost-Efficiency and Sustainable Growth 301
12.5.5 Outcomes and Achievements 302
12.5.5.1 Exponential Scalability 302
12.5.5.2 Precision in Delivery Operations 302
12.5.5.3 Data-Driven Decision-Making 302
12.5.5.4 Enhanced Customer Experiences 302
12.5.5.5 Operational Efficiency and Cost Savings 302
12.5.5.6 Innovation and Competitive Edge 303
12.6 Ethical Considerations 303
12.6.1 Data Privacy 303
12.6.2 Transparency and Explainability 304
12.6.3 Mitigating Bias and Fostering Fairness 305
12.6.4 Stakeholder Management and Accountability 305
12.7 Future Trends 306
12.7.1 Emerging Trends in AutoML 306
12.7.1.1 Enhanced Model Explainability 306
12.7.1.2 Democratization of AI Continues 306
12.7.1.3 Integration of AutoML with Edge Computing 306
12.7.1.4 Hybrid Cloud Deployments for Flexibility 306
12.7.1.5 AutoML for Structured and Unstructured Data 306
12.7.1.6 Integration of AutoML in Industry- Specific Solutions 307
12.7.1.7 Continuous Model Monitoring and Maintenance 307
12.7.1.8 Emphasis on Responsible AI and Ethical Considerations 307
12.7.1.9 Quantum Computing’s Impact on AutoML 307
12.7.2 Considerations for Implementation 307
12.7.2.1 Clearly Defined Objectives 307
12.7.2.2 Data Quality and Accessibility 308
12.7.2.3 Skillset and Training 308
12.7.2.4 Regulatory Compliance and Ethical Considerations 308
12.7.2.5 Data Security and Privacy 308
12.7.2.6 Integration with Existing Systems 308
12.7.2.7 Cost Considerations and ROI 308
12.7.2.8 Vendor Selection and Partnerships 309
12.7.2.9 Scalability and Futureproofing 309
12.7.2.10 Change Management and User Adoption 309
12.7.2.11 Continuous Monitoring and Optimization 309
12.8 Conclusion 309
References 310
13 AI Insights: Navigating Education News Ethically Through Aggregation and Sentiment Analysis 313
Anshumaan Garg and Dolly Sharma
13.1 Introduction 314
13.1.1 Basics of Sentiment Analysis 314
13.1.2 Data Scraping from Web 316
13.1.3 Vader 318
13.1.4 BeautifulSoup 319
13.1.5 Sentiment Analysis 320
13.1.6 Web Scraping 321
13.1.7 Scope 322
13.1.8 Objectives 322
13.1.9 Chapter Outline 322
13.2 Literature Review 323
13.2.1 Working of BeautifulSoup 323
13.2.2 API-Based Data Extraction 324
13.2.3 Natural Language Tool-Kit 325
13.2.4 Preprocessing 325
13.2.5 Working of Vader 326
13.3 Methodology 328
13.3.1 Creation of a Virtual Environment 328
13.3.2 Installation of Python Libraries 328
13.3.3 Code Editor Used for Programming 328
13.3.4 Commands Used 328
13.3.5 Django MVT Architecture 329
13.3.6 Modules and Functions Used 329
13.3.7 Working 331
13.3.7.1 News Aggregation 331
13.3.8 Sentiment Analysis 331
13.3.8.1 API User Verification 331
13.4 Results Discussion 334
13.4.1 View of Website 334
13.4.2 News Aggregator 335
13.4.3 Sentiment Analysis 336
13.4.4 Advantages 338
13.4.5 Disadvantages 338
13.5 Conclusion and Future Work 339
13.5.1 Conclusion 339
13.5.2 Future Work 340
References 340
14 Case Study on AI-Based Ethical Decision-Making for Smart Transportation 343
S. Muthu Lakshmi, K. Mythili, Malathy Sathyamoorthy, Rajesh Kumar Dhanaraj and Aanjan Kumar S.
14.1 Introduction 344
14.2 Artificial Intelligence 345
14.3 Role of Artificial Intelligence in Transportation 347
14.4 Literature Review 348
14.4.1 Autonomous Vehicle 348
14.4.2 Communication Between Vehicles 349
14.4.3 Tracking Using GPS 350
14.5 Challenges 351
14.6 AI Ethics 351
14.6.1 AI Smart Transportation Use Cases 353
14.6.1.1 Object Detection 354
14.6.1.2 Driver Monitoring 354
14.6.1.3 Route Prediction 354
14.6.1.4 Smart Traffic Lights 355
14.6.2 Ethics in Autonomous Vehicles 355
14.6.3 Managing Traffic and Congestion Prediction 356
14.6.4 Decision-Making Process in Smart Transportation Systems 357
14.7 Data Confidentiality and Security 360
14.8 Vision from Data: Smart Decision-Making in Transportation 361
14.9 Conclusions 363
14.10 Future Directions 363
References 364
15 Case Study on AI-Based Decision-Making in E-Commerce: Exploring Location-Based Insights for Analysis of Geospatial Data 367
Ashima Arya, Daksh Rampal, Ekagra, Kashish Varshney, Rohit Vashisht and Yonis Gulzar
15.1 Introduction 368
15.1.1 Method of Geospatial Data Analysis 369
15.2 Objective 372
15.3 Background Knowledge 372
15.4 Related Work 374
15.5 Data Analysis of Geolocation Data 378
15.6 Proposed Methodology 380
15.7 Results 384
15.8 Conclusion 387
15.9 Future 387
Acknowledgment 388
References 388
Index 393
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