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The use of artificial intelligence (AI) in research across all disciplines isbecoming ubiquitous. However, this ubiquity is largely driven by hyperspecificAI models developed during scientific studies for accomplishing a well-defined,data-dense task. These AI models introduce apparent, human-recognizable biasesbecause they are trained with finite, specific data sets and parameters.However, the efficacy of using large language models (LLMs) -- and LLM-poweredgenerative AI tools, such as ChatGPT -- to assist the research process iscurrently indeterminate. These generative AI tools, trained on general andimperceptibly large datasets along with human feedback, present challenges inidentifying and addressing biases. Furthermore, these models are susceptible togoal misgeneralization, hallucinations, and adversarial attacks such as redteaming prompts -- which can be unintentionally performed by human researchers,resulting in harmful outputs. These outputs are reinforced in research -- wherean increasing number of individuals have begun to use generative AI to composemanuscripts. Efforts into AI interpretability lag behind development, and theimplicit variations that occur when prompting and providing context to achatbot introduce uncertainty and irreproducibility. We thereby find thatincorporating generative AI in the process of writing research manuscriptsintroduces a new type of context-induced algorithmic bias and has unintendedside effects that are largely detrimental to academia, knowledge production,and communicating research.

Generative AI in Writing Research Papers: A New Type of Algorithmic Bias and Uncertainty in Scholarly Work