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ChatGPT and Generative Artificial Intelligence (AI): Attribution and the AID Framework

What is attribution and why is it important?

Attribution is a less formal approach for crediting ideas and material from others. In scholarly writing and presentation, it may take the form of a footnote or endnote, a section at the end of the paper where thanks and attribution are offered, or through the less formal citation processes around personal communications which are only communicated within the body of the text. Attribution forms a critical complement to citation as it is both a less structured and more flexible way to credit ideas, concepts, and contributions of others. Attribution also has other benefits as it can include additional information that may not be found in the citation itself, including licensing information, multiple hyperlinks, and a detailed description of how and why the material is being attributed in the work. In the context of generative artificial intelligence systems, attribution offers a path to transparent disclosure of the varied ways such tools may have been used in academic or research work. 

The Artificial Intelligence Disclosure (AID) Framework (2024) was developed by Kari D. Weaver, Learning, Teaching, and Instructional Design Librarian, to enhance transparency and consistency in attribution practices for generative artificial intelligence. Inquiries may be directed to kdweaver@uwaterloo.ca. 

The Artificial Intelligence Disclosure (AID) Framework

Artificial Intelligence Disclosure (AID) Framework[1]

The purpose of the Artificial Intelligence Disclosure (AID) Framework is to provide brief, targeted disclosure about the use of AI systems based on the range of activities used for research writing. The AID Statement is appended to the end of the paper (similar to an Acknowledgements section), detailing the AI tools used and the manner in which they were used, based on the possible points of engagement through the writing process, as captured in the headings below. The formatting is intended to be both human- and machine-readable, and uses the following structure:

AID Statement: Artificial Intelligence Tool: [description of tools used]; [Heading]: [description of AI use in that stage of the work];…

Each heading: statement pair will end in a semi-colon, except for the last statement, which will end in a period. Any other symbols can be used in “statement” portion of the heading: statement pair except for colons and semi-colons.

If AI tools were used at any point in the writing, research, or project management processes, the AID Statement will always begin with the “artificial intelligence tool” section. It will then be followed by any heading: statement pairs necessary to disclose AI tool use. Heading: statement pairs will only be included if AI was used in that portion of the writing process. If a heading is not needed, it should not be included. If AI was not used at any point in the writing, research, or project management processes, authors would not include an AID Statement in their work.

The potential headings for the AID Statement, and their definitions, are the following:

  1. Artificial Intelligence Tool(s): The selection of tool or tools and versions of those tools used and dates of use. May also include note of any known biases or limitations of the models or data sets.
  2. Conceptualization: The development of the research idea or hypothesis including framing or revision of research questions and hypotheses.
  3. Methodology: The planning for the execution of the study including all direct contributions to the study design.
  4. Information Collection: The use of artificial intelligence to surface patterns in existing literature and identify information relevant to the framing, development, or design of the study.
  5. Data Collection Method: The development or design of software or instruments used in the study.
  6. Execution: The direct conduct of research procedures or tasks (e.g. AI web scraping, synthetic surveys, etc.)
  7. Data Curation: The management and organization of those data.
  8. Data Analysis: The performance of statistical or mathematical analysis, regressions, text analysis, and more using artificial intelligence tools.
  9. Privacy and Security: The ways in which data privacy and security were upheld in alignment with the expectations of ethical conduct of research, disciplinary guidelines, and institutional policies.
  10. Interpretation: The use of artificial intelligence tools to categorize, summarize, or manipulate data and suggest associated conclusions.
  11. Visualization: The creation of visualizations or other graphical representations of the data.
  12. Writing – Review & Editing: The revision and editing of the manuscript.
  13. Writing – Translation: The use of artificial intelligence to translate text across languages at any point in the drafting process.
  14. Project Administration: Any administrative tasks related to the study, including managing budgets, timelines, and communications.
 

[1] As generative artificial intelligence tools may not be an author of scholarly work, overlap in categorization between CRediT and AID Framework have been edited as necessary to reflect this distinction.

Acknowledgements and Thanks

Many thanks to the members of the Associate Vice-President Academic's Standing Committee on New Technologies, Pedagogies, and Academic Integrity at the University of Waterloo for their encouragement and support of this work. Particular thanks for the following colleagues who provided thoughtful, constructive, and positive feedback throughout the development process:

  • Nadine Fladd, Writing and Communication Centre
  • Karen Lochead, formerly of the Centre for Extended Learning
  • Amanda McKenzie, Office of Academic Integrity
  • Trevor Holmes, Centre for Teaching Excellence

Citing the AID Framework

Weaver, K. D. (2024). The artificial intelligence disclosure (AID) framework: An introduction. arXiv. https://arxiv.org/abs/2408.01904

Additional Resources