Racial and Ethnic Bias in Letters of Recommendation and Personal Statements for Application to Otolaryngology Residency

Stack, Taylor J; Berk, Garrett A; Ho, Tiffany D; Zeatoun, Abdullah; Kong, Kyoung Ae; Chaskes, Mark B.; Thorp, Brian D.; Ebert, Charles S.; DeMason, Christine E; Senior, Brent A.; Kimple, Adam J.

doi:10.1159/000529795

Cited by 6 publications

(2 citation statements)

References 26 publications

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“…Additionally, although this editorial does not address other professional documents that may benefit from technological augmentation, there is evidence to suggest that biased language appears in evaluations of trainees [49], including subjective evaluations for students applying to residency programs [24]; qualitative evaluations of residents and students [12,50]; student, resident, and fellow evaluations of faculty physicians [9]; and more [51,52]. Racial bias in evaluations also is problematic [53][54][55].…”

Section: Discussionmentioning

confidence: 99%

Can AI Mitigate Bias in Writing Letters of Recommendation? (Preprint)

Leung,

Sagar,

Shroff

et al. 2023

Preprint

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UNSTRUCTURED 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.

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Section: Discussionmentioning

confidence: 99%

Can AI Mitigate Bias in Writing Letters of Recommendation? (Preprint)

Leung,

Sagar,

Shroff

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Additionally, although this editorial does not address other professional documents that may benefit from technological augmentation, there is evidence to suggest that biased language appears in evaluations of trainees [ 49 ], including subjective evaluations for students applying to residency programs [ 24 ]; qualitative evaluations of residents and students [ 12 , 50 ]; student, resident, and fellow evaluations of faculty physicians [ 9 ]; and more [ 51 , 52 ]. Racial bias in evaluations also is problematic [ 53 - 55 ].…”

Section: Discussionmentioning

confidence: 99%

Can AI Mitigate Bias in Writing Letters of Recommendation?

Leung,

Sagar,

Shroff

et al. 2023

JMIR Med Educ

View full text Add to dashboard Cite

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.

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Large Language Models in Otolaryngology Residency Admissions: A Random Sampling Analysis

Halagur,

Balakrishnan,

Ayoub

2024

The Laryngoscope

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ObjectivesTo investigate potential demographic bias in artificial intelligence (AI)‐based simulations of otolaryngology, residency selection committee (RSC) members tasked with selecting one applicant among candidates with varied racial, gender, and sexual orientations.MethodsThis study employed random sampling of simulated RSC member decisions using a novel Application Programming Interface (API) to virtually connect to OpenAI's Generative Pre‐Trained Transformers (GPT‐4 and GPT‐4o). Simulated RSC members with diverse demographics were tasked with ranking to match 1 applicant among 10 with varied racial, gender, and sexual orientations. All applicants had identical qualifications; only demographics of the applicants and RSC members were varied for each simulation. Each RSC simulation ran 1000 times. Chi‐square tests analyzed differences across categorical variables. GPT‐4o simulations additionally requested a rationale for each decision.ResultsSimulated RSCs consistently showed racial, gender, and sexual orientation bias. Most applicant pairwise comparisons showed statistical significance (p < 0.05). White and Black RSCs exhibited greatest preference for applicants sharing their own demographic characteristics, favoring White and Black female applicants, respectively, over others (all pairwise p < 0.001). Asian male applicants consistently received lowest selection rates. Male RSCs favored White male and female applicants, while female RSCs preferred LGBTQIA+, White and Black female applicants (all p < 0.05). High socioeconomic status (SES) RSCs favored White female and LGBTQIA+ applicants, while low SES RSCs favored Black female and LGBTQIA+ applicants over others (all p < 0.001). Results from the newest iteration of the LLM, ChatGPT‐4o, indicated evolved selection preferences favoring Black female and LGBTQIA+ applicants across all RSCs, with the rationale of prioritizing inclusivity given in >95% of such decisions.ConclusionUtilizing publicly available LLMs to aid in otolaryngology residency selection may introduce significant racial, gender, and sexual orientation bias. Potential for significant and evolving LLM bias should be appreciated and minimized to promote a diverse and representative field of future otolaryngologists in alignment with current workforce data.Level of EvidenceN/A Laryngoscope, 2024

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Racial and Ethnic Bias in Letters of Recommendation and Personal Statements for Application to Otolaryngology Residency

Cited by 6 publications

References 26 publications

Can AI Mitigate Bias in Writing Letters of Recommendation? (Preprint)

Can AI Mitigate Bias in Writing Letters of Recommendation? (Preprint)

Can AI Mitigate Bias in Writing Letters of Recommendation?

Large Language Models in Otolaryngology Residency Admissions: A Random Sampling Analysis

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