Can Bard, Google's Experimental Chatbot Based on the LaMDA Large Language Model, Help to Analyze the Gender and Racial Diversity of Authors in Your Cited Scientific References?

Abstract

There is a growing recognition that scientific articles featuring women and people of color as first and last (senior) author are undercited in the literature relative to male and non-minority race authors. Some limited tools now exist to analyze the diversity of manuscript bibliographies, with acknowledged limitations. Recently the journal editors and publications chair of the Biomedical Engineering Society have recommended that authors include an optional "Citation Diversity Statement" in their articles, however adoption of this practice has, to date, been slow. Inspired by the current excitement and enthusiasm for artificial intelligence (AI) large language model chatbots, I sought to determine whether Google's new Bard chatbot could be used to assist authors in this process. It was determined that the Bard technology is not yet up to this task, however, by showing some modest improvement in the fidelity of references, combined with the not-yet realized live search capabilities, the author is nevertheless optimistic that this technology can one day be utilized for this purpose as it continues to improve.