The Cambridge Handbook of the Law, Ethics and Policy of Artificial Intelligence By Nathalie Smuha
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About this ebook
The Cambridge Handbook of the Law, Ethics and Policy of Artificial Intelligence, edited by Nathalie A. Smuha, provides an interdisciplinary, comprehensive analysis of AI governance with a strong focus on European regulatory frameworks. The text is structured into three foundational parts: [1, 2, 3, 4]
Part I: AI, Ethics and Philosophy
The opening section establishes the theoretical and technical foundations required to understand the broader societal impacts of machine learning. [1]
- Technical Foundations: Structural differences and development paths between machine learning and machine reasoning.
- Philosophy of AI: Conceptual overviews examining agency, intelligence, and the limits of autonomous decision-making.
- Design for Values: Methodologies like Value-Sensitive Design to proactively build ethical constraints into AI development.
- Fairness & Bias: Technical and philosophical approaches to addressing systemic data discrimination and algorithmic bias.
- Moral Responsibility: Assessing accountability gaps when autonomous technologies generate unpredictable real-world outcomes.
- Power and Sustainability: Examining the data economics of power, energy use, and environmental costs of training massive models. [1, 2, 3]
Part II: AI, Law and Policy
This part shifts toward legal instruments, analyzing how current laws handle algorithmic systems and outlining emergent global compliance structures. [1, 2]
- Privacy and Data Protection: Intersecting machine learning requirements with fundamental human rights under frameworks like the EU GDPR. [1]
- Competition Law: Identifying market risks like automated algorithmic collusion and anti-competitive supply chain behaviors. [1]
- Intellectual Property (IP) Law: Debating liability for copyright infringement and whether AI models can legally qualify as authors or inventors. [1]
- The EU AI Act: Comprehensive teardown of risk-based categorization, compliance obligations, and exemptions for research or defense. [1]
Part III: AI across Sectors
The final section shifts from horizontal regulation to vertical, application-specific fields to look at context-dependent effects. [1]
- Law Enforcement: Analyzing the governance, efficacy, and civil liberties impacts of predictive policing and automated target identification.
- Education: Addressing the disruption of schools by generative AI, the Beijing Consensus, and institutional policies for responsible tool deployment.
- Healthcare & Public Sector: Managing transparency, diagnostic safety, and administrative automation in public institutions. [1, 2]
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