Responsible AI
Description
Bridge the gap between groundbreaking AI innovation and ethical responsibility with this comprehensive guide to the expert-led frameworks needed to navigate the complex legal, social, and moral landscapes of our digital future.
Artificial Intelligence (AI) has emerged as a transformative force with the ability to bring new innovations to reshape economies, industries, and our daily lives. From advanced medical diagnostics to autonomous vehicles, AI systems are driving incomparable innovations in every sector. These advancements promise unmatched benefits and provide the potential to solve some of humanity's most pressing challenges. However, there are many potential challenges and significant risks that come alongside the benefits provided by AI.
This book offers a multidisciplinary viewpoint on how to develop and use AI systems responsibly by offering a deep dive into the ethical, legal, and societal ramifications of artificial intelligence. It explores important subjects such as algorithmic fairness, transparency, accountability, and governance through contributions from notable academics, engineers, and policy specialists. It highlights how crucial it is to match AI development with democratic norms and human values, offering both theoretical frameworks and workable implementation solutions for a range of industries. This comprehensive guide is an essential resource for scholars, professionals, and legislators dedicated to making sure that AI technology is created and applied in ways that are moral, inclusive, and advantageous to society.
The reader will find the volume:
- Provides a multidisciplinary exploration of the ethical, legal, and social dimensions of AI;
- Bridges the gap between AI theory and real-world applications through practical frameworks;
- Covers key topics such as fairness, transparency, accountability, and governance;
- Serves as a valuable resource for researchers, practitioners, and policymakers aiming to build trustworthy AI systems.
Audience
AI practitioners, data scientists, developers, business leaders, and executives actively engaged in the development and implementation of AI systems.
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Persons
Manish Kumar, PhD is an Assistant Professor at the Thapar Institute of Engineering and Technology, India, with more than eight years of teaching experience. He has authored several scientific articles in international journals and conferences, as well as internationally published books and book chapters. His research interests include soft computing applications for bioinformatics problems and computational intelligence.
Nitigya Sambyal, PhD is an Assistant Professor in the Department of Computer Science and Engineering at the Thapar Institute of Engineering and Technology, India. She is also a postdoctoral fellow in the Department of Information Technology at Uppsala University, Sweden. Her research interests include machine learning, deep learning, medical image analysis, and computer vision.
Leena Priyadarshini Singh, PhD is an Assistant Professor in Organizational Behavior and Industrial Relations with more than 14 years of experience. She has published more than 30 research papers in refereed international journals and several chapters in edited books. Her research interests include quality of work life, work-life balance, strategic leadership, corporate governance, and corporate social responsibility.
Ramasamy V., PhD is an Associate Professor in the Dr. Sagunthala Research and Development Institute of Science and Technology, Vel Tech Rangarajan, India. He is the author of several scholarly research papers in national and international journals and conferences and editor of several books. His areas of interest include mobile cloud computing, IoT, data science, artificial intelligence, and data mining.
S. Balamurugan, PhD is the Director of Research at iRCS, an Indian Technological Research and Consulting firm with more than 20 years of experience. He has published more than 100 books, 300 papers in international journals and conferences, and 300 patents. He specializes in technology forecasting and decision-making for leading companies and startups.
Content
Series Preface xxi
Preface xxiii
Acknowledgement xxvii
1 AI for Social Good 1
R. Srivats, Kalyanasundaram V., Abhiram Sharma, Deepika Roselind J. and Logeswari G.
1.1 Introduction to AI for Social Good 2
1.2 AI in Healthcare 6
1.3 AI in Education 10
1.4 AI for Disaster Management and Response 14
1.5 AI in Culture 17
1.6 Conclusion and Future Work 24
2 Balancing Innovation and Patient Safety: Ethical AI Deployment in Healthcare 29
Prajakta R. Patil, Sachin S. Mali, Riya R. Patil and Dhanashree R. Davare
2.1 Introduction 29
2.2 The Promise of AI in Healthcare 34
2.3 Ethical Challenges in AI 36
2.4 Responsible AI Development and Deployment 39
2.5 Case Studies: Real-World Examples of Ethical AI in Healthcare 46
2.6 Strategies for Ensuring Ethical and Responsible Use of AI 52
2.7 The Future of Ethical AI in Healthcare 56
2.8 Conclusion 58
3 Responsible AI in Practice: Case Studies from Industry and Government 69
Nabanita Roy, Sangita Roy and Shalini Kumari
3.1 Introduction 69
3.2 Framework for Analyzing Responsible AI Implementation 71
3.3 Literature Review 72
3.4 Case Studies 72
3.5 Cross-Sector Analysis: Patterns in Responsible AI Implementation 74
3.6 Emerging Regulatory Landscape 75
3.7 Recommendations for Organizations 75
3.8 Discussion 78
3.9 Conclusion 79
4 An Efficient System for Skin Disease Detection and Localization Using Faster Region Based Convolutional Neural Networks with Inception Architecture 81
Nitin Singh, Ankita Nanda, Keshav Garg, Varun Gupta, Nitigya Sambyal and Deepika Vikas Agrawal
4.1 Introduction 82
4.2 Related Work 84
4.3 Proposed System 86
4.4 Results 96
4.5 Conclusion 100
5 Detection of Machining Error Using Intelligent Hybrid Machine Learning Technique 105
Ritu Maity
5.1 Introduction 106
5.2 Literature Review 106
5.3 Models Used 108
5.4 Methodology 109
5.5 Results and Discussion 113
5.6 Conclusion 116
6 Ground Water Level Classification Using Machine Learning 119
Charu Chaudhary, Khushi Passi, Taruna Saini, Ritika Dhaneshwar and Varun Gupta
6.1 Introduction 120
6.2 Related Work 121
6.3 Data Description and Data Processing 124
6.4 Results and Discussion 130
6.5 Conclusion 139
7 Sustainability in AI Development 143
Riya R. Patil, Sandip A. Bandgar, Sachin S. Mali, Prajakta R. Patil and Dhanashree R. Davare
7.1 Introduction 144
7.2 Environmental Sustainability in AI 148
7.3 Social Sustainability in AI 152
7.4 Economic Sustainability in AI 157
7.5 Governance and Policy for Sustainable AI 159
7.6 Challenges and Future Directions 164
7.7 Conclusion and Call to Action 167
8 Integrating AutoML and Explainability: A Unified Approach for Decision-Making in Engineering and Social Sciences 175
Ayush Dalmia and Chandramohan Dhasarathan
8.1 Introduction 176
8.2 Literature Study 178
8.3 Proposed Model 183
8.4 Evaluation of the Proposed System (Comparative Analysis/Justification with Acceptable Measures/Metrics) 186
8.5 Observations 195
8.6 Conclusion 196
9 Trust Dynamics and Ethical Transparency in AI-Powered Mobile Apps: A DatäDriven Exploration of User Perceptions 199
Rachita Sambyal
9.1 Introduction 200
9.2 Review of Literature 201
9.3 Research Methodology 204
9.4 Results and Discussion 204
9.5 Results and Recommendations 212
9.6 Limitations and Future Scope 212
9.7 Conclusion 212
10 AI-Powered Advancements in Autonomous Vehicle Technologies 221
Sachi Choudhary and Prashant Shukla
10.1 Introduction 222
10.2 Core AI Technologies for AVs 224
10.3 Machine Learning and Deep Learning Techniques for AVs 226
10.4 Computer Vision and Image Processing in AVs 228
10.5 Sensor Fusion and Environmental Perception in AVs 231
10.6 Object Detection and Classification in Autonomous Vehicles (AVs) 233
10.7 Decision-Making and Path Planning in AVs 235
10.8 AI's Role in Route Optimization, Path Planning, and Obstacle Avoidance 239
10.9 Challenges of AI in Autonomous Vehicles 240
10.10 Conclusion 241
11 Data Security and Privacy Frameworks for AI Technologies 247
Sangita Roy and Nabanita Roy
11.1 Introduction 248
11.2 Foundations of Data Security and Privacy in AI 249
11.3 Challenges in AI-Specific Privacy and Security 251
11.4 Privacy-Preserving AI Technologies 251
11.5 Regulatory and Legal Frameworks 255
11.6 Organizational Privacy and Security Frameworks 258
11.7 Case Studies 259
11.8 Designing Privacy-Centric AI Systems 261
11.9 Future Directions 264
11.10 Conclusion 266
12 AI in Autonomous Systems 269
Kalyanasundaram V., G. Prethija, Keerthi A.J., Yuvan Shankar Baabu and R. Srivats
12.1 Introduction to AI in Autonomous Systems 270
12.2 AI Technologies in Autonomous Systems 274
12.3 Autonomous Vehicles and Real-Time Decision Making 279
12.4 AI Innovations in Space and Healthcare Systems 284
12.5 Safety, Ethical Considerations, and Challenges 288
12.6 Future Directions and Conclusion 291
13 Responsible Use of AI in Healthcare: Addressing Bias, Transparency, and Patient Trust 297
Shubham Gupta
13.1 Introduction 298
13.2 Ethical Challenges in AI-Driven Healthcare 300
13.3 Transparency in AI Systems 306
13.4 Building and Maintaining Patient Trust 311
13.5 Governance and Regulatory Oversight 314
13.6 The Future of Ethical AI in Healthcare 318
14 Advancing Healthcare with AI: Balancing Efficiency, Security, and Compliance 323
Sivakumar Ramakrishnan
14.1 Introduction 324
14.2 Literature Review 329
14.3 Identified Gaps in Literature and Future Directions 332
14.4 Methodology 333
14.5 Result and Discussion 345
14.6 Case Studies and Real-World Examples 351
14.7 Ethical Considerations in AI-Based Healthcare Fraud Detection 353
14.8 Blockchain and Federated Learning: Securing AI-Based Healthcare Transactions Blockchain in Healthcare Transactions 357
14.9 AI's Limitations and the Evolution of Fraud Strategies 357
14.10 Conclusion 359
15 AI Beyond the Veil: Techniques for Privacy Preservation 363
D. Kalpanadevi
15.1 Introduction 364
15.2 Scope of Research 364
15.3 Background 365
15.4 Techniques for Privacy Preservation 365
15.5 Implementation and Discussion 373
15.6 Current Challenges 375
15.7 Industry Adoption 376
15.8 Future Directions 377
15.9 Conclusion 377
References 378
16 VetAce - A Deep Learning Inspired Framework for Classification and Prediction of Pet Diseases 379
Munish Saini, Vaibhav Arora and Harpreet Singh
16.1 Introduction 380
16.2 Related Work 381
16.3 Analysis Methodology 383
16.4 Results and Analysis 391
16.5 Discussion 395
16.6 Conclusion 396
Bibliography 397
Index 401