Responsible AI : Principles and Practices.

Format:

Book

Author/Creator:

Kumar, Manish.

Language:

English

Subjects (All):

Artificial intelligence.

Ethics.

Physical Description:

1 online resource (441 pages)

Edition:

1st ed.

Place of Publication:

Newark : John Wiley & Sons, Incorporated, 2026.

Summary:

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.

Contents:

Cover

Series Page

Title Page

Copyright Page

Dedication

Contents

Series Preface

Preface

Acknowledgement

Chapter 1 AI for Social Good

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

Chapter 2 Balancing Innovation and Patient Safety: Ethical AI Deployment in Healthcare

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

Chapter 3 Responsible AI in Practice: Case Studies from Industry and Government

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

Chapter 4 An Efficient System for Skin Disease Detection and Localization Using Faster Region Based Convolutional Neural Networks with Inception Architecture

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

Chapter 5 Detection of Machining Error Using Intelligent Hybrid Machine Learning Technique

5.1 Introduction

5.2 Literature Review

5.3 Models Used

5.4 Methodology

5.5 Results and Discussion

5.6 Conclusion

Chapter 6 Ground Water Level Classification Using Machine Learning

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

Chapter 7 Sustainability in AI Development

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

Chapter 8 Integrating AutoML and Explainability: A Unified Approach for Decision-Making in Engineering and Social Sciences

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

Chapter 9 Trust Dynamics and Ethical Transparency in AI-Powered Mobile Apps: A Data-Driven Exploration of User Perceptions

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

Annexture I

Annexture II

Annexture III

Questionnaire

Chapter 10 AI-Powered Advancements in Autonomous Vehicle Technologies

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

Chapter 11 Data Security and Privacy Frameworks for AI Technologies

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.

Notes:

Description based on publisher supplied metadata and other sources.

Part of the metadata in this record was created by AI, based on the text of the resource.

ISBN:

1-394-35547-5

1-394-35546-7

9781394355464

OCLC:

1579270664