Responsible AI

Principles and Practices

Manish Kumar, Nitigya Sambyal, Leena P. Singh, V. Ramasamy, S. Balamurugan

• Publisher: Wiley-Scrivener
• Year: 2026
• Print ISBN:
  9781394355440
• Edition: 1
• eBook ISBN:
  9781394355457
• Topic: Computer Science
• Subtopic: Artificial Intelligence (AI) & Semantics
• Index: Computer Science

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.

Table of contents

• Cover
• Table of Contents
• Series Page
• Title Page
• Copyright Page
• Dedication
• Series Preface
• Preface
• Acknowledgement
• 1 AI for Social Good
• 2 Balancing Innovation and Patient Safety: Ethical AI Deployment in Healthcare
• 3 Responsible AI in Practice: Case Studies from Industry and Government
• 4 An Efficient System for Skin Disease Detection and Localization Using Faster Region Based Convolutional Neural Networks with Inception Architecture
• 5 Detection of Machining Error Using Intelligent Hybrid Machine Learning Technique
• 6 Ground Water Level Classification Using Machine Learning
• 7 Sustainability in AI Development
• 8 Integrating AutoML and Explainability: A Unified Approach for Decision-Making in Engineering and Social Sciences
• 9 Trust Dynamics and Ethical Transparency in AI-Powered Mobile Apps: A Data-Driven Exploration of User Perceptions
• 10 AI-Powered Advancements in Autonomous Vehicle Technologies
• 11 Data Security and Privacy Frameworks for AI Technologies
• 12 AI in Autonomous Systems
• 13 Responsible Use of AI in Healthcare: Addressing Bias, Transparency, and Patient Trust
• 14 Advancing Healthcare with AI: Balancing Efficiency, Security, and Compliance
• 15 AI Beyond the Veil: Techniques for Privacy Preservation
• 16 VetAce – A Deep Learning Inspired Framework for Classification and Prediction of Pet Diseases
• Index
• Also of Interest
• End User License Agreement