Generative AI Ethics, Privacy, and Security
Wrick Talukdar
This audiobook is narrated by a digital voice.
DESCRIPTION
Generative AI is transforming industries globally, with the majority of organizations using generative AI in at least one business function. From the fundamental evolution of transformer models to the complex ethical questions they raise, this book equips readers with the knowledge to navigate AI with confidence.
This book begins by introducing foundational concepts of generative AI and transformer model evolution, along with architectures, including GANs and autoencoders. It explores ethical frameworks and societal impacts, examines privacy challenges in data usage and generated content, and addresses security threats with mitigation strategies. Readers will learn responsible development and governance practices, navigate the legal and regulatory landscape, and learn how to educate users about AI capabilities and limitations. The book concludes with real-world case studies, best practices for deployment, and future directions for ethical innovation.
Upon completing this book, readers will possess the knowledge and skills to lead generative AI initiatives, balancing innovation with ethical responsibility.
WHAT YOU WILL LEARN
● Explore transformer models, GANs, and autoencoder architectures.
● Implement ethical AI frameworks and bias mitigation strategies.
● Design privacy-preserving systems for sensitive data handling.
● Deploy security measures against adversarial attacks and misuse.
● Navigate global AI regulations and compliance requirements.
● Build responsible governance structures for AI deployment.
● Educate stakeholders on AI capabilities and limitations.
● Apply best practices through real-world case studies.
Duration - 12h 56m.
Author - Wrick Talukdar.
Narrator - Digital Voice Madison G.
Published Date - Tuesday, 14 January 2025.
Copyright - © 2026 BPB ©.
Location:
United States
Description:
This audiobook is narrated by a digital voice. DESCRIPTION Generative AI is transforming industries globally, with the majority of organizations using generative AI in at least one business function. From the fundamental evolution of transformer models to the complex ethical questions they raise, this book equips readers with the knowledge to navigate AI with confidence. This book begins by introducing foundational concepts of generative AI and transformer model evolution, along with architectures, including GANs and autoencoders. It explores ethical frameworks and societal impacts, examines privacy challenges in data usage and generated content, and addresses security threats with mitigation strategies. Readers will learn responsible development and governance practices, navigate the legal and regulatory landscape, and learn how to educate users about AI capabilities and limitations. The book concludes with real-world case studies, best practices for deployment, and future directions for ethical innovation. Upon completing this book, readers will possess the knowledge and skills to lead generative AI initiatives, balancing innovation with ethical responsibility. WHAT YOU WILL LEARN ● Explore transformer models, GANs, and autoencoder architectures. ● Implement ethical AI frameworks and bias mitigation strategies. ● Design privacy-preserving systems for sensitive data handling. ● Deploy security measures against adversarial attacks and misuse. ● Navigate global AI regulations and compliance requirements. ● Build responsible governance structures for AI deployment. ● Educate stakeholders on AI capabilities and limitations. ● Apply best practices through real-world case studies. Duration - 12h 56m. Author - Wrick Talukdar. Narrator - Digital Voice Madison G. Published Date - Tuesday, 14 January 2025. Copyright - © 2026 BPB ©.
Language:
English
Title Page
Duration:00:00:21
Copyright Page
Duration:00:01:21
Dedication Page
Duration:00:00:57
About the Authors
Duration:00:03:12
About the Reviewer
Duration:00:01:23
Acknowledgements
Duration:00:02:02
Preface
Duration:00:08:51
Table of Contents
Duration:00:13:50
1. Introduction to Generative AI
Duration:00:00:05
Introduction
Duration:00:01:35
Structure
Duration:00:00:18
Objectives
Duration:00:01:01
An overview of generative AI
Duration:00:04:17
Difference between deep learning and machine learning
Duration:00:01:41
Evolution and development
Duration:00:05:37
Rise of transformers
Duration:00:03:37
Rise of generative AI
Duration:00:02:07
Applications and implications
Duration:00:09:56
Future prospects and challenges
Duration:00:03:17
Conclusion
Duration:00:01:51
Key takeaways
Duration:00:01:46
References
Duration:00:02:12
2. Foundations of Transformers, GANs, and Other Generative Models
Duration:00:00:06
Working of transformers
Duration:00:03:24
Basics of encoder-decoder
Duration:00:01:34
Encoder models
Duration:00:02:57
Decoder models
Duration:00:02:53
Encoder-decoder models
Duration:00:03:23
Applications of encoder and decoder in real life
Duration:00:12:24
GAN, autoencoder, and autoregression
Duration:00:00:28
Generative adversarial networks
Duration:00:04:12
Autoencoders
Duration:00:07:54
Autoregression
Duration:00:04:59
Training and tuning language models
Duration:00:03:40
Training a machine learning model
Duration:00:05:28
Fine-tuning a pre-trained model
Duration:00:02:05
Instruction fine-tuning
Duration:00:03:34
In-context learning
Duration:00:03:29
Retrieval augmented generation
Duration:00:02:31
Data considerations
Duration:00:04:28
3. Ethical Considerations in Generative AI
Duration:00:00:06
Ethical principles in AI development
Duration:00:01:06
Fairness
Duration:00:08:16
Transparency
Duration:00:07:47
Accountability
Duration:00:13:02
Explainability
Duration:00:09:58
Moral dilemmas in generative AI
Duration:00:08:37
Societal impacts of generative AI
Duration:00:01:05
Industries and employment
Duration:00:01:51
Cultural aspects and the rethinking of creativity
Duration:00:01:51
Changing human interaction and communication
Duration:00:01:49
Addressing societal impacts and ethical considerations
Duration:00:01:32
Regulatory and policy perspectives
Duration:00:04:33
Responsible deployment and future directions
Duration:00:04:51
4. Privacy Challenges and Implications
Duration:00:00:05
Data privacy in AI
Duration:00:02:36
Privacy risks in generated content
Duration:00:03:53
Disclosure of personal or private information
Duration:00:00:52
Context-assisted generation
Duration:00:02:20
Non-context-assisted generation
Duration:00:01:35
Data leakage
Duration:00:02:47
Realistic but false information
Duration:00:01:15
Privacy violations
Duration:00:02:07
Deepfakes and synthetic media
Duration:00:01:50
Malicious actors
Duration:00:01:22
Chatbots and virtual assistants
Duration:00:00:48
Data usage and privacy concerns
Duration:00:00:24
Data collection
Duration:00:01:46
Data storage
Duration:00:03:32
Responsible handling of sensitive information
Duration:00:03:26
User privacy preservation techniques
Duration:00:16:09
Privacy-utility tradeoff
Duration:00:04:07
Legal and regulatory perspectives
Duration:00:01:53
5. Security Risks and Mitigation Strategies
Duration:00:00:05
Security threats in generative AI
Duration:00:00:26
Adversarial attacks
Duration:00:02:58
Underlying mechanisms of adversarial attacks
Duration:00:03:39
Data poisoning
Duration:00:01:57
Data poisoning in generative AI systems
Duration:00:03:37
Model inversion attacks
Duration:00:03:23
Model extraction attacks
Duration:00:03:44
Overfitting and data leakage
Duration:00:03:39
Preventing overfitting and data leakage in generative AI
Duration:00:05:37
Potential misuse and ethical concerns
Duration:00:00:39
Addressing the issues
Duration:00:01:48
Robustness and adversarial defense
Duration:00:03:34
Adversarial training
Duration:00:03:53
Robust model architectures
Duration:00:03:41
Defensive mechanisms
Duration:00:04:07
Data security and access control
Duration:00:00:28
Encryption methods
Duration:00:05:19
Secure data storage
Duration:00:00:57
Access management
Duration:00:01:45
6. Responsible Development and Governance
Duration:00:00:05
Sustainable development
Duration:00:03:09
Cost implications and compute requirements
Duration:00:02:31
Cost-effective alternatives
Duration:00:01:23
Smaller models
Duration:00:01:33
Fine-tuning
Duration:00:01:46
Model pruning
Duration:00:01:40
Quantization
Duration:00:02:49
Stakeholder engagement and collaboration
Duration:00:00:25
Data scientists and machine learning engineers
Duration:00:08:45
Business and technology leaders
Duration:00:00:25
Establishing ethical guidelines and policies
Duration:00:02:13
Creating oversight mechanisms
Duration:00:02:07
Strategic decision-making
Duration:00:02:03
Stakeholder engagement
Duration:00:01:49
Promoting ethical culture
Duration:00:01:38
Auditors and policymakers
Duration:00:00:26
Auditors’ role in AI governance
Duration:00:03:18
Policymakers’ role in AI regulation
Duration:00:03:57
Challenges and opportunities
Duration:00:01:35
End users
Duration:00:00:22
Importance of end-user feedback
Duration:00:01:22
Engaging with end users
Duration:00:08:31
Challenges and considerations
Duration:00:01:29
Continuous monitoring and auditing
Duration:00:00:34
Need for continuous monitoring
Duration:00:02:24
Auditing generative AI models
Duration:00:09:03
Re-training and model updates
Duration:00:01:09
Implementing LLMOps/MLOps practices
Duration:00:01:19
Capturing data points for audit trail
Duration:00:01:20
Challenges in continuous monitoring and auditing
Duration:00:01:40
Emerging trends and best practices
Duration:00:04:30
The role of human oversight
Duration:00:00:52
7. Legal and Regulatory Landscape of AI Systems
Duration:00:00:06
Reviewing existing laws, regulations, and policies
Duration:00:01:12
European Union
Duration:00:00:47
AI Act
Duration:00:03:17
General Data Protection Regulation
Duration:00:05:07
United States
Duration:00:00:55
Blueprint for an AI Bill of Rights
Duration:00:03:58
China
Duration:00:05:06
Algorithmic accountability and transparency
Duration:00:08:17
Interpretability
Duration:00:00:56
Interpreting text generation
Duration:00:04:33
Interpreting image generation
Duration:00:02:21
Interpreting other generative tasks
Duration:00:00:42
Key challenges in generative AI explainability
Duration:00:02:05
Safety standards and reporting requirements
Duration:00:04:31
Indemnity implications
Duration:00:05:35
Key AI regulatory frameworks
Duration:00:00:57
8. User Awareness and Education
Duration:00:00:04
Capabilities and limitations
Duration:00:01:30
Capabilities of generative AI
Duration:00:00:41
Creating text content
Duration:00:05:54
Generating visual media
Duration:00:03:12
Simulating conversations
Duration:00:01:00
Personalization
Duration:00:01:03
Automating creativity
Duration:00:00:58
Limitations of generative AI
Duration:00:00:42
Understanding context deeply
Duration:00:01:23
Creativity constraints
Duration:00:01:03
Accuracy issues
Duration:00:01:02
Lack of ethical judgment
Duration:00:01:48
Inability to learn from interactions
Duration:00:00:43
Limited multimodal integration
Duration:00:00:39
Lack of common sense reasoning
Duration:00:01:42
Applicability and constraints of use
Duration:00:00:24
Content authenticity
Duration:00:02:39
Addressing the challenges
Duration:00:01:10
Intellectual property
Duration:00:02:46
Challenges in IP protection
Duration:00:04:46
Addressing these challenges
Duration:00:01:12
Potential misuse
Duration:00:02:46
Privacy concerns
Duration:00:01:59
Accountability and responsibility
Duration:00:01:57
Societal impact
Duration:00:02:32
Emotional and psychological effects
Duration:00:01:50
Environmental considerations
Duration:00:02:32
Optimal use cases
Duration:00:02:44
Scenarios to avoid
Duration:00:03:41
Data privacy and security awareness
Duration:00:00:56
Importance of data privacy
Duration:00:03:53
Legal and privacy concerns
Duration:00:03:32
Navigating misinformation and bias
Duration:00:01:02
Challenges of AI-generated content
Duration:00:02:45
Identifying and managing risks
Duration:00:03:30
Public trust and social impact
Duration:00:00:56
Building trust in AI
Duration:00:03:30
Positive social impact
Duration:00:04:16
9. Case Studies
Duration:00:00:03
End-to-end content creation with generative AI
Duration:00:03:39
Problem or challenge
Duration:00:00:21
Approach and implementation
Duration:00:00:32
Key components
Duration:00:03:19
Results and impact
Duration:00:03:19
Personalized learning using generative AI
Duration:00:02:16
Generative AI in personalized learning
Duration:00:05:19
Enterprise RAG chatbots
Duration:00:03:32
The knowledge management dilemma
Duration:00:01:49
Working
Duration:00:00:53
Key features and benefits
Duration:00:01:34
Use cases
Duration:00:00:47
Technical considerations
Duration:00:01:51
Achieving cost efficiency
Duration:00:01:06
Ensuring compliance and accuracy
Duration:00:01:01
Real-world impact across industries
Duration:00:00:58
Measuring success and ROI
Duration:00:00:38