Artificial Intelligence and Law: Challenges Demystified (Second Edition) provides a comprehensive exploration of how artificial intelligence is transforming legal systems, regulatory approaches, and governance globally. As AI increasingly shapes decision-making, commerce, surveillance, and social interactions, the book examines how the law is evolving to address accountability, transparency, fairness, and risk management in algorithm-driven contexts.
This revised edition incorporates recent technological developments and legal responses, analyzing how lawmakers, courts, and regulators confront AI’s impact on privacy, data protection, security, competition, consumer rights, and ethical governance. Through practical examples and case studies, the authors clarify complex legal issues and illustrate how legal frameworks can stay effective, adaptable, and forward-thinking in the era of intelligent systems.
Key Features:
- Introduces the technical principles of AI and machine learning for a legal audience
- Covers emerging regulations, guidelines, and governance frameworks for AI
- Explores legal responsibility and accountability in AI-driven decision-making
- Assesses AI’s effects on privacy, data protection, surveillance, and security
- Discusses ethical issues including bias, discrimination, transparency, and explainability
- Provides real-world examples and industry-specific case studies
- Highlights international and comparative approaches to regulating AI
- Examines future trends and policy strategies for responsible AI implementation
This book is intended for legal professionals, judges, in-house lawyers, policymakers, regulators, tech specialists, compliance officers, academics, researchers, and postgraduate students working at the crossroads of law, technology, and governance.
CONTENTS :
CHAPTER 1 : LIST OF Abbreviations
INTRODUCTION TO ARTIFICIAL INTELLIGENCE
1.1 Artificial Intelligence as a Natural Person
1.2 Legal personhood and artificially intelligent machine
1.3 Brief History of Artificial Intelligence
1.4 Understanding the Technicalities
1.4.1 Machine Learning
1.4.2 Computer Vision
1.4.3 Cognitive Computing
1.4.4 Deep Learning
1.4.5 Neural Network
1.5 Challenges
1.6 Concluding Remarks
CHAPTER 2 : REGULATION OF ARTIFICIAL INTELLIGENCE
2.1 Challenges with Nomenclature and Definition of AI
2.2 Conceptualisation Issues around AI
2.2.1 Autonomy
2.2.2 Foreseeability and Causation
2.2.3 Control
2.2.4 Opacity in Research and Development
2.3 Soft Law vs Hard Law
2.4 Regulatory Frameworks for AI
2.4.1 The United States of America
2.4.2 The People’s Republic of China
2.4.3 The European Union
2.4.4 The Republic of India
2.4.5 The United Kingdom
2.5 Need for an ethical approach towards regulation
CHAPTER 3 : ARTIFICIAL INTELLIGENCE AND INTELLECTUAL PROPERTY RIGHTS: NAVIGATING INNOVATIONS, CHALLENGES, AND LEGAL IMPLICATIONS
3.1 Background
3.2 Understanding Legal Personhood
3.3 AI and Copyright
3.3.1 The Legal Side
3.3.2 Development and Sophistication of Artificial Intelligence
3.3.3 Artificial Intelligence Conflict with the Copyright Law
3.3.3.1 Computer-Generated Works and the Concept of Authorship
3.3.3.2 Originality Threshold for Computer-Generated Works
3.4 Artificial Intelligence and Trade Marks
3.4.1 Relevance of AI in trade mark law
3.4.2 Adaptation of AI in Trade mark law
3.5 AI and Patents
3.5.1 Genetic Programming
3.5.2 Artificial Neural Networks
3.5.3 Robot Scientists
3.6 Patentability of AI-assisted Inventions
3.7 Unravelling the Enigma: The quest for sui generis rights in AI creation
3.7.1 Defining AI Outputs
3.7.2 Ensuring the integrity of AI training data
3.7.3 Towards a harmonised legal framework on copyright
3.7.4 Who holds the rights?
CHAPTER 4 : ARTIFICIAL INTELLIGENCE AND PRIVACY
4.1 Education
4.1.1 Trustworthy AI: The prerequisite to realising the full potential
4.1.2 Online Exam Proctoring
4.1.3 Virtual Learning Classrooms
4.1.4 Content Creation by GenAI
4.1.5 Dealing with the Privacy Concerns: A Multi-Jurisdictional View
4.1.6 Conclusion
4.2 Governance
4.2.1 Citizen Profiling
4.2.2 Public-Private Partnership
4.2.3 Fuelling Inequality
4.2.4 Lack of Transparency
4.3 FinTech and TechFins
4.3.1 The changing dynamics of the financial ecosystem
4.3.2 Towards a secured FinTech
4.3.3 Data Usage Protection and Risk Reduction
4.3.4 Current legal safeguards and its effectiveness
4.3.5 Way Forward: Collaboration of regulators and FinTech companies
4.4 Health
4.4.1 Integrating AI and Healthcare
4.4.2 Informed Consent in Medical AI
4.4.3 Navigating through the Data Collection Policy
4.4.4 Health Data Disparity and Social Exclusion
CHAPTER 55 : ARTIFICIAL INTELLIGENCE AND DIGITAL MARKETPLACES: ANALYSING THROUGH THE LENS OF ANTITRUST REGULATIONS
5.1 Interface between Artificial Intelligence and Competition Law: Setting the Context
5.2 Effect of Artificial Intelligence on Competition
5.3 Understanding ‘Agreement’ and ‘Relevant Market’
5.3.1 Agreement
5.3.2 Relevant Market
5.4 Regulatory Models & Market Overview: Developments across the globe
5.4.1 United Kingdom
5.4.2 European Union
5.4.3 The United States of America
5.4.4 India
5.5 Competition Challenges for a Digital Market
5.5.1 Role of Data in Digitisation
5.5.1.1 Algorithmic Discriminatory Pricing
a. Geo-Targeting
b. Dark Patterns
c. Self-preferencing via Unfair Ranking and Design Algorithms
d. Pricing and Dominance Strategy
5.5.1.2 Algorithmic Collusion
5.6 AI and Mergers
5.6.1 Effect and Object Test
5.7 Addressing the Regulatory Potholes
5.8 The Road Ahead: Moving towards a competition by design approach
5.9 Conclusion
CHAPTER 6 : ARTIFICIAL INTELLIGENCE AND THE FUTURE: EXPLORING THE FRONTIERS OF TOMORROW’S INTELLIGENCE
6.1 Financial Impact
6.2 Education
6.3 Virtual Lectures
6.4 Communication
6.5 Law Enforcement and Military
6.6 AI Applications for Defence
6.6.1 Intelligence, Surveillance, and Reconnaissance
6.6.2 Logistics
6.6.3 Cyberspace Operations
6.6.4 Information Operations and “Deepfakes”
6.6.5 Semi-Autonomous and Autonomous Vehicles
6.6.6 Predictive Policing
6.7 Building Sustainable AI
6.8 Judiciary
6.8.1 Indian Context
6.8.2 Multi-Jurisdictional view of use of AI in Judiciary
6.8.3 Predictive Justice and AI: Challenges Ahead
6.8.4 Towards a better integration
6.9 Legal Technology
6.9.1 Legal Research
6.9.2 Contract Analytics and Review
6.9.3 Due Diligence
6.9.4 Litigation Prediction
6.9.5 Intellectual Property Management
6.9.6 Drawbacks
