[Paper Review] Development of the ChatGPT, Generative Artificial Intelligence and Natural Large Language Models for Accountable Reporting and Use (CANGARU) Guidelines

Giovanni Cacciamani, Michael Eppler+5 more|arXiv (Cornell University)|Jul 18, 2023

Artificial Intelligence in Healthcare and Education14 citations

TL;DR

This paper presents the CANGARU initiative to develop global, cross-disciplinary guidelines for ethical use, disclosure, and reporting of GAI/GPT/LLM in academia, through a four-part protocol.

ABSTRACT

The swift progress and ubiquitous adoption of Generative AI (GAI), Generative Pre-trained Transformers (GPTs), and large language models (LLMs) like ChatGPT, have spurred queries about their ethical application, use, and disclosure in scholarly research and scientific productions. A few publishers and journals have recently created their own sets of rules; however, the absence of a unified approach may lead to a 'Babel Tower Effect,' potentially resulting in confusion rather than desired standardization. In response to this, we present the ChatGPT, Generative Artificial Intelligence, and Natural Large Language Models for Accountable Reporting and Use Guidelines (CANGARU) initiative, with the aim of fostering a cross-disciplinary global inclusive consensus on the ethical use, disclosure, and proper reporting of GAI/GPT/LLM technologies in academia. The present protocol consists of four distinct parts: a) an ongoing systematic review of GAI/GPT/LLM applications to understand the linked ideas, findings, and reporting standards in scholarly research, and to formulate guidelines for its use and disclosure, b) a bibliometric analysis of existing author guidelines in journals that mention GAI/GPT/LLM, with the goal of evaluating existing guidelines, analyzing the disparity in their recommendations, and identifying common rules that can be brought into the Delphi consensus process, c) a Delphi survey to establish agreement on the items for the guidelines, ensuring principled GAI/GPT/LLM use, disclosure, and reporting in academia, and d) the subsequent development and dissemination of the finalized guidelines and their supplementary explanation and elaboration documents.

Motivation & Objective

• Motivate the need for a unified, cross-disciplinary standard for ethical use and disclosure of GAI/GPT/LLM in academia.
• Describe the CANGARU initiative and its goal to foster inclusive global consensus.
• Outline a four-part protocol to develop, validate, and disseminate guidelines.
• Address risks of a Babel Tower effect from inconsistent publisher rules and reporting practices.

Proposed method

• Four-part protocol: ongoing systematic review of GAI/GPT/LLM applications and reporting standards in scholarly research.
• Bibliometric analysis of existing author guidelines in journals mentioning GAI/GPT/LLM to assess disparities and identify common rules.
• Delphi survey to establish agreement on guideline items ensuring principled use, disclosure, and reporting.
• Development and dissemination of finalized guidelines with supplementary explanation and elaboration documents.

Experimental results

Research questions

• RQ1 What are current reporting standards and ideas associated with GAI/GPT/LLM in scholarly research?
• RQ2 How do existing author guidelines vary across journals that mention GAI/GPT/LLM, and what common rules emerge?
• RQ3 What consensus items should comprise the CANGARU guidelines for ethical use and disclosure of GAI/GPT/LLM in academia?
• RQ4 How can the finalized guidelines be effectively disseminated and adopted across disciplines?

Key findings

• The paper defines a four-part protocol for developing accountable reporting guidelines for GAI/GPT/LLM in academia.
• It proposes a systematic review, bibliometric analysis, Delphi survey, and dissemination plan as the core methodology.
• The approach aims to prevent a Babel Tower effect by harmonizing disparate publisher rules.
• The initiative seeks to establish a cross-disciplinary global inclusive consensus on ethical use, disclosure, and reporting of GAI/GPT/LLM in scholarly work.