Identifying and Mitigating Gender Cues in Academic Recommendation Letters: An Interpretability Case Study

TL;DR

This study investigates gender bias in academic recommendation letters using transformer models, revealing persistent gender cues even after de-gendering and emphasizing the need for auditing evaluative texts for fairness.

Contribution

The paper demonstrates the presence of implicit gender cues in recommendation letters and evaluates methods to mitigate gender bias, providing a framework for fairer evaluation practices.

Findings

Gender cues in LoRs can be detected with up to 68% accuracy.

Removing gender cues reduces classifier accuracy by up to 5.5%.

Gender prediction remains above chance even after de-gendering.

Abstract

Letters of recommendation (LoRs) can carry patterns of implicitly gendered language that can inadvertently influence downstream decisions, e.g. in hiring and admissions. In this work, we investigate the extent to which Transformer-based encoder models as well as Large Language Models (LLMs) can infer the gender of applicants in academic LoRs submitted to an U.S. medical-residency program after explicit identifiers like names and pronouns are de-gendered. While using three models (DistilBERT, RoBERTa, and Llama 2) to classify the gender of anonymized and de-gendered LoRs, significant gender leakage was observed as evident from up to 68% classification accuracy. Text interpretation methods, like TF-IDF and SHAP, demonstrate that certain linguistic patterns are strong proxies for gender, e.g. "emotional'' and "humanitarian'' are commonly associated with LoRs from female applicants. As an experiment in creating truly gender-neutral LoRs, these implicit gender cues were remove resulting in a drop of up to 5.5% accuracy and 2.7% macro $F_1$ score on re-training the classifiers. However, applicant gender prediction still remains better than chance. In this case study, our findings highlight that 1) LoRs contain gender-identifying cues that are hard to remove and may activate bias in decision-making and 2) while our technical framework may be a concrete step toward fairer academic and professional evaluations, future work is needed to interrogate the role that gender plays in LoR review. Taken together, our findings motivate upstream auditing of evaluative text in real-world academic letters of recommendation as a necessary complement to model-level fairness interventions.