Appendix B — AI Coauthorship and Responsible Disclosure

Generative AI tools are increasingly integrated into research, data analysis, and academic writing. This appendix provides practical guidance on when and how to disclose AI use in work produced for courses, publications, and professional practice.

B.1 When Disclosure Is Required

You should disclose AI use when:

You used an LLM (ChatGPT, Claude, Gemini, etc.) to generate code, text, analysis, or visualizations that appear in your submitted work
You used LLM APIs (as in Chapter 13) as analytical tools — for sentiment analysis, content coding, structured extraction, or similar tasks
You used AI-powered code completion tools (GitHub Copilot, Cursor) for significant code generation beyond individual line completions

B.2 When Disclosure Is Typically Not Required

Using AI for spell-checking, grammar correction, or basic proofreading
Using AI to understand error messages or debug syntax errors (analogous to searching Stack Overflow)
Using standard autocomplete features in code editors

B.3 How to Disclose: A Practical Framework

An effective disclosure statement answers four questions:

What tool? Name the specific model and version (e.g., “GPT-4o via the OpenAI API, accessed in March 2026”)
What task? Describe what the AI was used for (e.g., “sentiment classification of 200 bill summaries,” “initial draft of the data collection section”)
What oversight? Describe how you reviewed, verified, and modified the output (e.g., “all generated code was tested independently,” “LLM classifications were validated against a 20% manual sample”)
What limitations? Note any concerns about the output’s accuracy or appropriateness

B.4 Example Disclosure Statements

For a notebook assignment:

AI tools used: GitHub Copilot was used for code autocompletion throughout the notebook. ChatGPT (GPT-4o) was consulted to debug a BeautifulSoup parsing error in Section 3. All code was tested and validated independently.

For a research paper:

Text embeddings were computed using OpenAI’s text-embedding-3-small model. Sentiment classifications were generated by Claude Sonnet 4 (claude-sonnet-4-20250514) using the prompting strategy described in Section 3.2. A random sample of 50 classifications (25%) was manually validated, achieving 88% agreement with human coding.

For a final project:

This project used AI tools in three ways: (1) GitHub Copilot for code autocompletion, (2) GPT-4o-mini for classifying 500 legislative bill summaries into policy categories using the few-shot prompt in Appendix B, and (3) Claude for copy-editing the final report. All classifications were spot-checked against a 50-item manual sample (92% agreement). The scraping code, analysis pipeline, and visualization code were written by the author.

B.5 Publisher and Funder Requirements

Major academic publishers and funding agencies have established disclosure requirements:

Elsevier, Springer Nature, Wiley, and Taylor & Francis all require disclosure of generative AI use but do not permit AI as a listed author. The consensus: AI tools can assist research, but humans bear full responsibility for accuracy and integrity.
NIH (NOT-OD-23-149) prohibits peer reviewers from using AI tools to analyze grant applications, citing confidentiality concerns. Researchers are encouraged to disclose AI use in project descriptions.
NSF suggests disclosure of AI use in proposals and will formalize requirements in future Proposal and Award Policies and Procedures Guides.

The landscape of publisher and funder policies continues to evolve. Always check the current requirements of your target journal or funding agency before submission.

B.6 Core Principles

Three principles underlie responsible AI disclosure:

Accountability is unchanged. AI tools do not change the fundamentals of scholarly accountability. You are responsible for the accuracy, originality, and integrity of everything you submit, regardless of what tools you used to produce it.
Disclosure is about transparency, not permission. The goal is not to ask whether you are “allowed” to use AI, but to ensure that readers can evaluate your work knowing how it was produced. Could someone replicate your results knowing what tools you used and how?
The failure to disclose is the problem. Using AI tools responsibly and transparently is generally acceptable. Using them without disclosure, or in ways that misrepresent the origins of your work, compromises research integrity.

Missing Manual Reference

For the full AI disclosure statement used in this book’s companion resource, see Missing Manual Appendix B: Disclosure — Use of AI in Authoring.

# AI Coauthorship and Responsible Disclosure {#sec-appendix-ai-disclosure} Generative AI tools are increasingly integrated into research, data analysis, and academic writing. This appendix provides practical guidance on when and how to disclose AI use in work produced for courses, publications, and professional practice. ## When Disclosure Is Required You should disclose AI use when: - You used an LLM (ChatGPT, Claude, Gemini, etc.) to generate code, text, analysis, or visualizations that appear in your submitted work - You used LLM APIs (as in @sec-ai-apis) as analytical tools — for sentiment analysis, content coding, structured extraction, or similar tasks - You used AI-powered code completion tools (GitHub Copilot, Cursor) for significant code generation beyond individual line completions ## When Disclosure Is Typically Not Required - Using AI for spell-checking, grammar correction, or basic proofreading - Using AI to understand error messages or debug syntax errors (analogous to searching Stack Overflow) - Using standard autocomplete features in code editors ## How to Disclose: A Practical Framework An effective disclosure statement answers four questions: 1. **What tool?** Name the specific model and version (e.g., "GPT-4o via the OpenAI API, accessed in March 2026") 2. **What task?** Describe what the AI was used for (e.g., "sentiment classification of 200 bill summaries," "initial draft of the data collection section") 3. **What oversight?** Describe how you reviewed, verified, and modified the output (e.g., "all generated code was tested independently," "LLM classifications were validated against a 20% manual sample") 4. **What limitations?** Note any concerns about the output's accuracy or appropriateness ## Example Disclosure Statements **For a notebook assignment:** > AI tools used: GitHub Copilot was used for code autocompletion throughout the notebook. ChatGPT (GPT-4o) was consulted to debug a BeautifulSoup parsing error in Section 3. All code was tested and validated independently. **For a research paper:** > Text embeddings were computed using OpenAI's `text-embedding-3-small` model. Sentiment classifications were generated by Claude Sonnet 4 (`claude-sonnet-4-20250514`) using the prompting strategy described in Section 3.2. A random sample of 50 classifications (25%) was manually validated, achieving 88% agreement with human coding. **For a final project:** > This project used AI tools in three ways: (1) GitHub Copilot for code autocompletion, (2) GPT-4o-mini for classifying 500 legislative bill summaries into policy categories using the few-shot prompt in Appendix B, and (3) Claude for copy-editing the final report. All classifications were spot-checked against a 50-item manual sample (92% agreement). The scraping code, analysis pipeline, and visualization code were written by the author. ## Publisher and Funder Requirements Major academic publishers and funding agencies have established disclosure requirements: - **Elsevier, Springer Nature, Wiley, and Taylor & Francis** all require disclosure of generative AI use but do not permit AI as a listed author. The consensus: AI tools can assist research, but humans bear full responsibility for accuracy and integrity. - **NIH** (NOT-OD-23-149) prohibits peer reviewers from using AI tools to analyze grant applications, citing confidentiality concerns. Researchers are encouraged to disclose AI use in project descriptions. - **NSF** suggests disclosure of AI use in proposals and will formalize requirements in future Proposal and Award Policies and Procedures Guides. The landscape of publisher and funder policies continues to evolve. Always check the current requirements of your target journal or funding agency before submission. ## Core Principles Three principles underlie responsible AI disclosure: 1. **Accountability is unchanged.** AI tools do not change the fundamentals of scholarly accountability. You are responsible for the accuracy, originality, and integrity of everything you submit, regardless of what tools you used to produce it. 2. **Disclosure is about transparency, not permission.** The goal is not to ask whether you are "allowed" to use AI, but to ensure that readers can evaluate your work knowing how it was produced. Could someone replicate your results knowing what tools you used and how? 3. **The failure to disclose is the problem.** Using AI tools responsibly and transparently is generally acceptable. Using them without disclosure, or in ways that misrepresent the origins of your work, compromises research integrity. ::: {.callout-tip} ## Missing Manual Reference For the full AI disclosure statement used in this book's companion resource, see *Missing Manual* Appendix B: Disclosure — Use of AI in Authoring. :::