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Responsible AI

Principles and Practices

Edited by Manish Kumar, Nitigya Sambyal, Leena Priyadarshini Singh, Ramasamy V and S. Balamurugan
Series: Leading-Edge Breakthroughs in Artificial Intelligence
Copyright: 2026   |   Status: Published
ISBN: 9781394355440  |  Hardcover  |  
448 pages
Price: $225 USD
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One Line 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.

Audience
AI practitioners, data scientists, developers, business leaders, and executives actively engaged in the development and implementation of AI systems.

Description
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.

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Author / Editor Details
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.

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Table of Contents
Series Preface
Preface
Acknowledgement
1. AI for Social Good
R. Srivats, Kalyanasundaram V., Abhiram Sharma, Deepika Roselind J. and Logeswari G.
1.1 Introduction to AI for Social Good
1.1.1 Social Good and the Role of AI
1.1.2 Ethical Frameworks and Considerations in the Implementation of AI
1.1.3 Consensus Between Public and Private Sectors
1.1.4 Challenges and Opportunities in AI for Social Good
1.2 AI in Healthcare
1.2.1 AI for Early Disease Detection and Diagnostics
1.2.2 AI-Powered Mental Health and Telemedicine Services
1.2.3 Ethical Concerns in Healthcare AI
1.3 AI in Education
1.3.1 AI for Adaptive Learning Systems and Personalized Learning
1.3.1.1 Introduction of Adaptive Learning Systems
1.3.1.2 AI and Personalized Learning
1.3.1.3 Key Technologies Supporting Adaptive and Personalized Learning
1.3.1.4 Benefits of AI-Powered Adaptive Systems
1.3.2 AI-Based Solutions Against Educational Inequalities
1.3.2.1 More Accessible AI Tools
1.3.2.2 Narrowing the Digital Divide
1.3.2.3 Personalized Learning for Different Learners
1.4 AI for Disaster Management and Response
1.4.1 Predictive Analytics for Disaster Preparedness
1.4.1.1 Predictive Analytics Application
1.4.2 AI in Coordination of Relief Efforts
1.4.2.1 Key Contributions of AI in Coordination
1.5 AI in Culture
1.5.1 Enhancement of Cultural Preservation Using AI
1.5.1.1 Digital Archiving and Restoration
1.5.1.2 Case Study: Google Arts and Culture
1.5.1.3 Preserving Oral Traditions
1.5.1.4 Predictive Analytics for Heritage Protection
1.5.2 AI’s Role in Responsible Promotion of Arts and Creativity
1.5.2.1 Enhancing Accessibility
1.5.2.2 Supporting Artists through AI Tools
1.5.2.3 Case Study: DALL-E
1.5.2.4 Ethical Considerations in Promotion
1.6 Conclusion and Future Work
1.6.1 Conclusion
1.6.2 Future Directions for AI in Social Good
References
2. Balancing Innovation and Patient Safety: Ethical AI Deployment in Healthcare
Prajakta R. Patil, Sachin S. Mali, Riya R. Patil and Dhanashree R. Davare
2.1 Introduction
2.1.1 Prominent Applications of AI in Healthcare
2.1.2 The Need for Ethical and Responsible Use
2.2 The Promise of AI in Healthcare
2.2.1 Improving Patient Care and Operational Efficiency
2.2.2 Expanding Access to Healthcare
2.3 Ethical Challenges in AI
2.3.1 Fairness and Bias in AI Algorithms
2.3.2 Transparency and Explainability
2.3.3 Privacy and Data Security
2.4 Responsible AI Development and Deployment
2.4.1 Designing Ethical AI Systems
2.4.2 Regulatory Framework and Governance
2.4.3 Collaboration between AI Systems and Healthcare Professionals
2.5 Case Studies: Real-World Examples of Ethical AI in Healthcare
2.5.1 Positive Applications of AI in Healthcare
2.5.2 Challenges and Ethical Dilemmas in AI Use
2.5.3 Best Practices and Lessons Learned
2.6 Strategies for Ensuring Ethical and Responsible Use of AI
2.6.1 Equity-Aware AI Systems
2.6.2 Promoting Transparency and Explainability
2.6.3 Establishing Accountability Mechanisms
2.7 The Future of Ethical AI in Healthcare
2.7.1 Evolving AI Technologies in Healthcare
2.7.2 Integration of AI with Next-Gen Technologies: Internet of Medical Things (IoMT), Wearable Health Tech, and More
2.7.3 Ongoing Ethical Considerations
2.8 Conclusion
2.8.1 Summary of Key Ethical Issues
2.8.2 Vision for the Future
Bibliography
3. Responsible AI in Practice: Case Studies from Industry and Government
Nabanita Roy, Sangita Roy and Shalini Kumari
3.1 Introduction
3.2 Framework for Analyzing Responsible AI Implementation
3.3 Literature Review
3.4 Case Studies
3.5 Cross-Sector Analysis: Patterns in Responsible AI Implementation
3.6 Emerging Regulatory Landscape
3.7 Recommendations for Organizations
3.8 Discussion
3.9 Conclusion
References
4. An Efficient System for Skin Disease Detection and Localization Using Faster Region Based Convolutional Neural Networks with Inception Architecture
Nitin Singh, Ankita Nanda, Keshav Garg, Varun Gupta, Nitigya Sambyal and Deepika Vikas Agrawal
4.1 Introduction
4.2 Related Work
4.3 Proposed System
4.3.1 Dataset
4.3.2 Methodology
4.3.2.1 Feature Extraction
4.3.2.2 Faster R-CNN Framework
4.3.3 Performance Metrics
4.3.3.1 Confusion Matrix
4.3.3.2 Precision and Recall
4.3.3.3 F-Score
4.4 Results
4.5 Conclusion
References
5. Detection of Machining Error Using Intelligent Hybrid Machine Learning Technique
Ritu Maity
5.1 Introduction
5.2 Literature Review
5.3 Models Used
5.4 Methodology
5.5 Results and Discussion
5.6 Conclusion
References
6. Ground Water Level Classification Using Machine Learning
Charu Chaudhary, Khushi Passi, Taruna Saini, Ritika Dhaneshwar and Varun Gupta
6.1 Introduction
6.2 Related Work
6.3 Data Description and Data Processing
6.3.1 Proposed Approach
6.4 Results and Discussion
6.5 Conclusion
Bibliography
7. Sustainability in AI Development
Riya R. Patil, Sandip A. Bandgar, Sachin S. Mali, Prajakta R. Patil and Dhanashree R. Davare
7.1 Introduction
7.1.1 Relevance and Urgency
7.1.2 Challenges and Opportunities in AI Development
7.1.2.1 Environmental Impacts of AI Development
7.1.2.2 Economic Impact of AI Development
7.2 Environmental Sustainability in AI
7.2.1 Energy Consumption and Carbon Footprint in AI
7.2.2 Efforts to Reduce Environmental Impact
7.2.3 Green AI Movement
7.3 Social Sustainability in AI
7.3.1 Fairness and Bias in AI Systems
7.3.2 Equity in Access to AI Technology
7.3.3 Impact on Workforce and Communities
7.4 Economic Sustainability in AI
7.4.1 Reducing Costs without Compromising Quality
7.4.2 AI Applications for Sustainable Development Goals (SDGs)
7.5 Governance and Policy for Sustainable AI
7.5.1 Global Standards and Frameworks
7.5.2 Transparency and Accountability
7.6 Challenges and Future Directions
7.6.1 Current Challenges
7.6.2 Overcoming Gaps in Global Cooperation and Regulation
7.6.3 Future Innovations
7.7 Conclusion and Call to Action
References
8. Integrating AutoML and Explainability: A Unified Approach for Decision-Making in Engineering and Social Sciences
Ayush Dalmia and Chandramohan Dhasarathan
8.1 Introduction
8.2 Literature Study
8.3 Proposed Model
8.4 Evaluation of the Proposed System (Comparative Analysis/Justification with Acceptable Measures/Metrics)
8.5 Observations
8.6 Conclusion
Bibliography
9. Trust Dynamics and Ethical Transparency in AI-Powered Mobile Apps: A Data‑Driven Exploration of User Perceptions
Rachita Sambyal
9.1 Introduction
9.2 Review of Literature
9.2.1 Privacy
9.2.2 Accountability
9.2.3 Safety and Security
9.2.4 Transparency and Explainability
9.2.5 Fairness and Non-Discrimination
9.2.6 Human Control of Technology
9.2.7 Professional Responsibility
9.2.8 Promotion of Human Values
9.3 Research Methodology
9.4 Results and Discussion
9.5 Results and Recommendations
9.6 Limitations and Future Scope
9.7 Conclusion
References
Annexture I
Annexture II
Annexture III
Questionnaire
10. AI-Powered Advancements in Autonomous Vehicle Technologies
Sachi Choudhary and Prashant Shukla
10.1 Introduction
10.1.1 Evolution of AI Technology in the Automotive Industry
10.2 Core AI Technologies for AVs
10.3 Machine Learning and Deep Learning Techniques for AVs
10.3.1 ML Techniques
10.3.2 DL Techniques
10.3.3 Combining Machine and Deep Learning for Autonomous Vehicle Features
10.4 Computer Vision and Image Processing in AVs
10.4.1 CV Techniques
10.4.2 Image Processing Techniques
10.4.3 Combining Computer Vision and Image Processing for AV Features
10.5 Sensor Fusion and Environmental Perception in AVs
10.5.1 Sensor Fusion Techniques
10.5.2 Environmental Perception
10.5.3 Combining Sensor Fusion with Environmental Perception
10.6 Object Detection and Classification in Autonomous Vehicles (AVs)
10.6.1 Techniques for Object Detection and Classification
10.6.2 Real-World Applications and Examples
10.6.3 Challenges in Object Detection and Classification
10.7 Decision-Making and Path Planning in AVs
10.7.1 Path Planning Techniques
10.7.2 Decision-Making Algorithms
10.7.3 Real-World Examples and Accuracy Metrics
10.7.4 Barriers in Decision-Making and Path Planning
10.7.5 Opportunities for Research and Development
10.8 AI’s Role in Route Optimization, Path Planning, and Obstacle Avoidance
10.9 Challenges of AI in Autonomous Vehicles
10.10 Conclusion
References
11. Data Security and Privacy Frameworks for AI Technologies
Sangita Roy and Nabanita Roy
11.1 Introduction
11.2 Foundations of Data Security and Privacy in AI
11.2.1 Security Goals: The CIA Triad
11.2.2 Privacy Goals
11.3 Challenges in AI-Specific Privacy and Security
11.4 Privacy-Preserving AI Technologies
11.4.1 Differential Privacy
11.4.2 Federated Learning
11.4.3 Homomorphic Encryption
11.4.4 Secure Multi-Party Computation (SMPC)
11.5 Regulatory and Legal Frameworks
11.5.1 General Data Protection Regulation (GDPR – European Union)
11.5.2 California Consumer Privacy Act (CCPA – United States)
11.5.3 India’s Digital Personal Data Protection Act (DPDP – India)
11.6 Organizational Privacy and Security Frameworks
11.7 Case Studies
11.7.1 Apple’s Federated Learning for Siri
11.7.2 Google’s Differential Privacy in Chrome
11.7.3 Facebook–Cambridge Analytica Scandal
11.8 Designing Privacy-Centric AI Systems
11.8.1 Privacy by Design
11.8.2 Threat Modeling for Privacy Risks
11.8.3 Transparency Mechanisms
11.9 Future Directions
11.9.1 Explainable AI (XAI)
11.9.2 AI Auditing Tools
11.9.3 Unified Global Standards
11.10 Conclusion
References
12. AI in Autonomous Systems
Kalyanasundaram V., G. Prethija, Keerthi A.J., Yuvan Shankar Baabu and R. Srivats
12.1 Introduction to AI in Autonomous Systems
12.1.1 Understanding Autonomous Systems
12.1.2 AI’s Role in Enabling Autonomous Functionality
12.2 AI Technologies in Autonomous Systems
12.2.1 Machine Learning and Deep Learning for Perception
12.2.1.1 Object Detection and Classification
12.2.1.2 Semantic Segmentation for Scene Understanding
12.2.1.3 Sensor Data Integration with Deep Learning
12.2.2 Reinforcement Learning in Autonomous Navigation
12.2.2.1 Path Planning Using Reinforcement Learning
12.2.2.2 Reward Design and Policy Optimization
12.2.3 Sensor Fusion and Multi-Modal Data Processing
12.2.3.1 Kalman Filtering and Particle Filtering
12.2.3.2 Deep Sensor Fusion for Robust Perception
12.3 Autonomous Vehicles and Real-Time Decision Making
12.3.1 AI in Self-Driving Cars: Safety and Efficiency
12.3.1.1 Role of AI in Self-Driving Cars
12.3.1.2 Core Algorithms Driving Safety and Efficiency
12.3.2 Drone Decision Making in Delivery and Surveillance
12.3.2.1 Navigation and Path Planning
12.3.2.2 Object Detection and Recognition
12.3.2.3 Energy Management and Efficiency
12.3.2.4 Data Analysis and Real-Time Decisions
12.3.3 AI-Driven Autonomous Military Vehicles in Defense
12.3.3.1 Autonomy to the Environment and Terrain
12.3.3.2 Target Detection and Engagement
12.3.3.3 Communication and Swarm Intelligence
12.3.3.4 Defense Mechanisms Countermeasures
12.4 AI Innovations in Space and Healthcare Systems
12.4.1 AI-Driven Autonomous Systems for Space Exploration
12.4.2 Autonomous Systems in Healthcare
12.5 Safety, Ethical Considerations, and Challenges
12.5.1 Managing Safety and System Failures in Autonomous Operations
12.5.1.1 Safety in Autonomous Systems
12.5.1.2 Fault Tolerance and Health Monitoring
12.5.1.3 Cybersecurity in Autonomous Systems
12.5.1.4 Human-Machine Interaction (HMI)
12.5.1.5 Advanced Testing Frameworks
12.5.2 Ethical Concerns in Autonomous Technologies
12.5.2.1 Impact on Human Employment
12.5.2.2 Decision-Making and Accountability
12.5.2.3 Privacy and Data Security
12.5.2.4 Ethical Use in Sensitive Domains
12.5.3 Challenges in Autonomous Systems
12.6 Future Directions and Conclusion
12.6.1 Conclusion
12.6.2 Future of AI in Expanding Autonomous Applications
References
13. Responsible Use of AI in Healthcare: Addressing Bias, Transparency, and Patient Trust
Shubham Gupta
13.1 Introduction
13.1.1 Overview of AI in Healthcare
13.1.2 Importance of Ethical Considerations
13.1.2.1 Ethical Implications of AI in Healthcare
13.1.2.2 Balancing Innovation with Responsibility
13.1.3 Scope of the Chapter
13.2 Ethical Challenges in AI-Driven Healthcare
13.2.1 Privacy and Data Security
13.2.1.1 Handling Sensitive Medical Information
13.2.1.2 Challenges in Data Anonymization and Potential Breaches
13.2.2 Bias in AI Models
13.2.2.1 Causes of Bias in AI Models
13.2.2.2 Impact on Marginalized Communities and Healthcare Disparities
13.2.3 Accountability and Responsibility
13.2.3.1 Liability When AI Systems Fail
13.2.3.2 Complexities in AI-Mediated Decisions
13.3 Transparency in AI Systems
13.3.1 The “Black Box” Problem
13.3.1.1 Lack of Interpretability in AI Decision-Making
13.3.1.2 Implications for Clinicians and Patients
13.3.2 Explainable AI (XAI)
13.3.2.1 Advances in Explainability to Build Trust
13.3.2.2 Case Studies/Examples of Successful XAI Implementations
13.3.3 Regulatory Standards for Transparency
13.3.3.1 Current Frameworks and Gaps in AI Governance
13.3.3.2 Recommendations for Future Regulatory Approaches
13.4 Building and Maintaining Patient Trust
13.4.1 Trust as a Pillar of Healthcare
13.4.1.1 The Role of Trust in Patient-Provider Relationships
13.4.1.2 Impact of AI Systems on Trust
13.4.2 Human-AI Collaboration
13.4.2.1 Ensuring AI Supports Rather Than Replaces Clinicians
13.4.2.2 Importance of Involving Patients in AI-Related Decisions
13.5 Governance and Regulatory Oversight
13.5.1 Need for Proactive Governance
13.5.1.1 Current Policies and Ethical Standards
13.5.1.2 Identifying Gaps in Oversight Mechanisms
13.5.2 Collaboration Across Stakeholders
13.5.2.1 Role of Technologists, Healthcare Professionals, and Ethicists
13.6 The Future of Ethical AI in Healthcare
References
14. Advancing Healthcare with AI: Balancing Efficiency, Security, and Compliance
Sivakumar Ramakrishnan
14.1 Introduction
14.1.1 Role of AI in Healthcare Fraud Detection
14.1.2 Challenges in AI-Based Fraud Detection
14.2 Literature Review
14.2.1 Introduction to AI in Healthcare
14.2.2 Historical Evolution of AI in Healthcare
14.2.3 Current Applications of AI in Healthcare
14.2.3.1 AI in Diagnostics and Medical Imaging
14.2.3.2 AI in Predictive Analytics and Disease Prevention
14.2.3.3 AI in Personalized Medicine and Drug Discovery
14.2.4 Ethical Considerations in AI Healthcare Adoption
14.2.4.1 Bias and Fairness in AI Models
14.2.4.2 Data Privacy and Security
14.2.4.3 Explainability and Transparency
14.2.5 Operational Challenges in AI Implementation
14.2.5.1 Integration with Existing Healthcare Infrastructure
14.2.5.2 Economic and Cost Implications
14.2.5.3 Physician and Patient Acceptance of AI
14.3 Identified Gaps in Literature and Future Directions
14.4 Methodology
14.4.1 Proposed Framework
14.4.2 Dataset Overview
14.4.3 Data Preprocessing & Feature Engineering
14.4.3.1 Handling Missing Values
14.4.3.2 Feature Engineering
14.4.3.3 Categorical Encoding
14.4.3.4 Feature Scaling
14.4.3.5 Train-Test Split
14.4.4 Dealing with Class Imbalance
14.4.5 Machine Learning Models and Training
14.4.5.1 Logistic Regression
14.4.5.2 Random Forest
14.4.5.3 Support Vector Machine (SVM)
14.4.5.4 K-Nearest Neighbors (KNN)
14.4.5.5 Autoencoder
14.4.6 The Hybrid Approach
14.4.7 Hyperparameter Tuning
14.4.8 Evaluation Metrics
14.5 Result and Discussion
14.5.1 Model Performance Comparison
14.5.2 Confusion Matrix Analysis
14.5.2.1 Confusion Matrix for Logistic Regression & SVM
14.5.2.2 Confusion Matrix for KNN and Random Forest
14.5.2.3 Confusion Matrix for Autoencoder
14.5.3 Precision vs. Recall Trade-Off
14.5.4 ROC-AUC Score Interpretation
14.6 Case Studies and Real-World Examples
14.7 Ethical Considerations in AI-Based Healthcare Fraud Detection
14.7.1 Regulatory Gaps in AI-Powered Fraud Detection
14.8 Blockchain and Federated Learning: Securing AI-Based Healthcare Transactions Blockchain in Healthcare Transactions
14.9 AI’s Limitations and the Evolution of Fraud Strategies
14.10 Conclusion
References
15. AI Beyond the Veil: Techniques for Privacy Preservation
D. Kalpanadevi
15.1 Introduction
15.2 Scope of Research
15.3 Background
15.4 Techniques for Privacy Preservation
15.4.1 Federated Learning
15.4.2 Differential Privacy
15.4.3 Homomorphic Encryption
15.4.4 Synthetic Data Generation
15.5 Implementation and Discussion
15.6 Current Challenges
15.6.1 Challenges
15.7 Industry Adoption
15.8 Future Directions
15.9 Conclusion
References
16. VetAce – A Deep Learning Inspired Framework for Classification and Prediction of Pet Diseases
Munish Saini, Vaibhav Arora and Harpreet Singh
16.1 Introduction
16.2 Related Work
16.3 Analysis Methodology
16.3.1 Data Preprocessing and Filtering
16.3.2 Image Augmentation
16.3.3 Convolutional Neural Network (CNN) as Pet Diseases Classifier
16.3.3.1 Description of Convolutional Neural Network (CNN)
16.3.3.2 Design of CNN
16.3.3.3 Transfer Learning
16.3.3.4 Network Architecture
16.3.3.5 Cost and Optimization Function
16.4 Results and Analysis
16.4.1 VetAce: A Framework for Diagnosing Pet Disease and Breed Affirmation
16.4.2 Performance Metrics of the Model
16.5 Discussion
16.6 Conclusion
Bibliography
Index

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