Can AI Mitigate Bias in Writing Letters of Recommendation?
Document Type
Article
Publication Date
8-23-2023
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.
Recommended Citation
Leung, Tiffany I; Sagar, Ankita; Shroff, Swati; and Henry, Tracey L, "Can AI Mitigate Bias in Writing Letters of Recommendation?" (2023). Division of Internal Medicine Faculty Papers & Presentations. Paper 66.
https://jdc.jefferson.edu/internalfp/66
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
PubMed ID
37610808
Language
English
Comments
This article is the author's final published version in JMIR Medical Education, Volume 9, 2023, Article number e51494.
The published version is available at https://doi.org/10.2196/51494.
Copyright © Tiffany I Leung, Ankita Sagar, Swati Shroff, Tracey L Henry. Originally published in JMIR Medical Education (https://mededu.jmir.org), 23.08.2023.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Education, is properly cited. The complete bibliographic information, a link to the original publication on https://mededu.jmir.org/, as well as this copyright and license information must be included.