Can AI Mitigate Bias in Writing Letters of Recommendation

• Type of Material: Article
• Authors: Tiffany I. Leung, Southern Illinois University; Ankita Sagar, Creighton University; Swati Shroff, Thomas Jefferson University; Tracey Henry, Emory University
• Language: English
• Date: 2023-08-23
• Publisher: JMIR Publications
• Publication Version: Final Published Version
• Copyright Statement: ©Tiffany I Leung, Ankita Sagar, Swati Shroff, Tracey L Henry. Originally published in JMIR Medical Education (https://mededu.jmir.org), 23.08.2023.
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): https://doi.org/10.2196%2F51494
• Title of Journal or Parent Work: JMIR Medical Education
• Volume: 9
• Start Page: e51494
• Keywords: sponsorship; leadership; gender bias; career advancement; large language models; bias; promotion; implicit bias; artificial intelligence; letters of recommendation; tenure and promotion; medical education
• Research Categories: Education, Guidance and Counseling

Abstract

Letters of recommendation play a significant role in higher education and career progression, particularly for women and underrepresented groups in medicine and science. Already, there is evidence to suggest that written letters of recommendation contain language that expresses implicit biases, or unconscious biases, and that these biases occur for all recommenders regardless of the recommender’s sex. Given that all individuals have implicit biases that may influence language use, there may be opportunities to apply contemporary technologies, such as large language models or other forms of generative artificial intelligence (AI), to augment and potentially reduce implicit biases in the written language of letters of recommendation. In this editorial, we provide a brief overview of existing literature on the manifestations of implicit bias in letters of recommendation, with a focus on academia and medical education. We then highlight potential opportunities and drawbacks of applying this emerging technology in augmenting the focused, professional task of writing letters of recommendation. We also offer best practices for integrating their use into the routine writing of letters of recommendation and conclude with our outlook for the future of generative AI applications in supporting this task.