Cases for Research Integrity: Generative AI: Moral Reasoning in Research Integrity Classes

Abstract

Generative AI in research has triggered an ethical debate on how it uses might challenge long established codes and practices of research integrity. For example, a survey by Nature (Van Noorden and Perkel, 2023) of 1600 researchers highlighted among others the following topic of concern: Generative AI in research has the potential to introduce mistakes or inaccuracies into research texts, make the detection of fabrication or falsification of research more difficult or make plagiarism easier. It can bring biases into literature searches or embed bias or inequities into research texts. Other challenges when working with AI-based tools is the originality and the credits that can be given to the humans in the process, the accountability they have and the breach of IPR and copyright that is lurking in the shadows when using AI-based tools for creating content. Additionally, who owns the content done by AI? The creators that interact with the generative AI or the tech companies behind the algorithm? Confidentiality and privacy might be at stake when sensible research data is used in new AI-assisted workflows and training on the correct workflows for any personal, private, or sensible information becomes even more necessary. For sure, research processes as analyzing and publishing research data will change and generative AI will become embedded in the workflows. This raises questions around the energy consumption and carbon footprint of research. There is increasing need for students to discuss and practice research integrity with generative AI embedded and used in different research situations. In this collection, we provide case studies for moral reasoning and complementary material that can be easily adapted by lecturers to their research integrity classes.