Chapter 35: Further Reading

Annotated Bibliography

The following resources provide deeper exploration of the legal, policy, and compliance topics covered in this chapter. Resources are organized by topic area and annotated with descriptions of their scope and relevance.

Copyright and AI-Generated Works

1. U.S. Copyright Office, "Copyright Registration Guidance: Works Containing Material Generated by Artificial Intelligence" (Federal Register, 2023)

The foundational guidance document from the U.S. Copyright Office on the registrability of works containing AI-generated material. This document establishes the Office's position that human authorship is required for copyright protection and provides the framework for analyzing works that combine human and AI contributions. Essential reading for anyone working with AI-generated code in the United States.

Relevance: Sections 35.1, 35.2

2. Andres Guadamuz, "Artificial Intelligence and Copyright" (WIPO Magazine, 2017; updated perspectives in subsequent publications)

An accessible introduction to the intersection of AI and copyright law from a global perspective. Guadamuz, a reader in intellectual property law at the University of Sussex, provides a balanced analysis of how different jurisdictions approach AI authorship. While the original article predates modern AI coding tools, the legal framework it describes remains foundational. Look for his more recent publications for updated analysis.

Relevance: Sections 35.1, 35.2

3. Ryan Abbott, "The Reasonable Robot: Artificial Intelligence and the Law" (Cambridge University Press, 2020)

A comprehensive academic treatment of how existing legal frameworks apply to AI, including intellectual property, liability, and regulation. Abbott, who led the Artificial Inventor Project (seeking patent protection for AI-generated inventions), provides a thorough analysis of why current law struggles with AI and proposes frameworks for adaptation. The book covers patent and copyright issues in depth.

Relevance: Sections 35.2, 35.7

Open-Source Licensing

4. Heather Meeker, "Open (Source) for Business: A Practical Guide to Open Source Software Licensing" (3rd Edition, 2020)

The definitive practical guide to open-source licensing for software developers and businesses. Meeker, a leading open-source licensing attorney, explains license types, compatibility, compliance obligations, and common pitfalls in clear, accessible language. While it predates the AI coding tool era, the licensing fundamentals are essential for understanding the compliance challenges discussed in Section 35.3.

Relevance: Section 35.3

5. Software Freedom Conservancy, "The Copyleft Guide" (copyleft.org)

A comprehensive, community-maintained guide to copyleft licensing, with detailed analysis of GPL, LGPL, and AGPL compliance requirements. This resource is particularly useful for understanding the obligations that arise when copyleft-licensed code enters a codebase — whether through direct copying, AI generation, or dependency inclusion. Available free online.

Relevance: Section 35.3

6. The Linux Foundation, "Compliance Programs and Resources" (linuxfoundation.org)

The Linux Foundation provides extensive resources on open-source compliance, including the OpenChain Project (ISO/IEC 5230), which establishes an international standard for open-source license compliance programs. These resources are valuable for organizations building compliance processes that address AI-generated code alongside traditional open-source usage.

Relevance: Sections 35.3, 35.9

AI Regulation and Policy

7. European Union, "Regulation (EU) 2024/1689 — The AI Act" (Official Journal of the European Union, 2024)

The full text of the EU AI Act, the world's first comprehensive AI regulation. While dense legal text, the Act's risk-based classification system, transparency requirements, and obligations for high-risk AI systems directly affect how AI coding tools are used in regulated contexts. Focus on Title III (High-Risk AI Systems) and Title IV (Transparency Obligations) for the most relevant provisions.

Relevance: Section 35.8

8. National Institute of Standards and Technology (NIST), "Artificial Intelligence Risk Management Framework (AI RMF 1.0)" (2023)

NIST's voluntary framework for managing AI risks, developed through extensive public consultation. The framework provides a structured approach to identifying, assessing, and mitigating AI risks that complements the organizational policy guidance in Section 35.9. Particularly useful for U.S.-based organizations seeking a recognized governance framework.

Relevance: Sections 35.8, 35.9

Data Privacy and AI

9. European Data Protection Board (EDPB), Guidance on AI and Data Protection (various publications, 2020-present)

The EDPB has issued multiple opinions and guidance documents on the intersection of AI and GDPR. These documents address questions such as the lawful basis for AI training data processing, data protection impact assessments for AI systems, and the application of data subject rights to AI outputs. Essential for organizations processing EU personal data through AI tools.

Relevance: Section 35.5

10. Daniel Solove and Woodrow Hartzog, "Breached! Why Data Security Law Fails and How to Improve It" (Oxford University Press, 2022)

While not AI-specific, this book provides a comprehensive analysis of data security law that is directly relevant to understanding the data protection obligations that arise when code and data flow to AI services. The authors' critique of existing legal frameworks and proposals for improvement provide context for the evolving data privacy landscape described in Section 35.5.

Relevance: Section 35.5

AI and Software Development Practice

11. GitHub, "Research on AI Coding Tools: Productivity, Code Quality, and Developer Experience" (GitHub Blog and Research Publications, 2022-present)

GitHub has published extensive research on the impact of AI coding tools on developer productivity, code quality, and workflow. These publications provide empirical data that informs policy decisions about AI tool adoption. They also include analysis of code suggestion quality, including metrics on how often suggestions match existing public code — directly relevant to license compliance concerns.

Relevance: Sections 35.3, 35.4

12. IEEE Standards Association, "IEEE P2863 — Recommended Practice for Organizational Governance of Artificial Intelligence" (In development)

An emerging IEEE standard for AI governance at the organizational level. While still in development as of writing, the draft provides a structured approach to AI governance that goes beyond coding tools to encompass the full lifecycle of AI adoption. The standard addresses accountability, transparency, and risk management in ways that align with the organizational policy framework in Section 35.9.

Relevance: Section 35.9

Legal Analysis and Commentary

13. Mark A. Lemley and Bryan Casey, "Fair Learning" (Texas Law Review, 2021)

A landmark law review article analyzing whether AI training on copyrighted data constitutes fair use. The authors argue that machine learning from copyrighted works should generally be permissible under fair use doctrine, analogizing it to human learning. This analysis is directly relevant to understanding the legal foundation (or lack thereof) for claims that AI outputs derived from copyrighted training data are infringing.

Relevance: Sections 35.1, 35.2, 35.3

14. Pamela Samuelson, "Allocating Ownership Rights in Computer-Generated Works" (University of Pittsburgh Law Review, 1986; with updated perspectives in recent publications)

Samuelson's pioneering work on computer-generated works remains relevant four decades later. Her analysis of the policy considerations for allocating ownership of machine-created works provides a historical foundation for understanding current debates. Her more recent publications address how these considerations apply to modern generative AI.

Relevance: Section 35.2

15. World Intellectual Property Organization (WIPO), "Conversation on Intellectual Property and Artificial Intelligence" (WIPO Publications, 2019-present)

WIPO has convened a global conversation on AI and IP, producing consultation documents, session reports, and issue papers that canvass the full range of IP questions raised by AI. These documents represent the most comprehensive international survey of how different countries and stakeholders are approaching AI-related IP issues. The documents are freely available on the WIPO website.

Relevance: All sections

Staying Current

Given the rapid pace of change in AI law, the following ongoing resources are recommended for staying current:

• Stanford Human-Centered AI (HAI) — Regular policy briefs and research on AI governance
• Berkman Klein Center at Harvard — Research on AI and society, including IP and privacy
• AI Now Institute — Policy research and analysis on AI's social implications
• Electronic Frontier Foundation (EFF) — Advocacy and analysis on AI, copyright, and digital rights
• Open Source Initiative (OSI) — Ongoing work on defining "open-source AI" and its implications

Remember: This bibliography provides educational starting points. For legal decisions affecting your organization, always consult qualified legal counsel who can apply these principles to your specific facts and jurisdiction.