Identifying and Mitigating Gender Cues in Academic Recommendation Letters: An Interpretability Case Study
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, RoB
Record details
Published: 14 April 2026
Source: arXiv
Category: Research
Topics: Safety & alignment · Healthcare · Finance, VC & PE
Retrieved: 14 July 2026
Related evidence
These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.
CLIP Architecture for Abdominal CT Image-Text Alignment and Zero-Shot Learning: Investigating Batch Composition and Data Scaling
arXiv · 15 April 2026
Physiological and Semantic Patterns in Medical Teams Using an Intelligent Tutoring System
arXiv · 31 March 2026
Retrieval-Guided Generation for Safer Histopathology Image Captioning
arXiv · 27 April 2026
When Language Overwrites Vision: Over-Alignment and Geometric Debiasing in Vision-Language Models
arXiv · 7 May 2026
Autoregressive EHR Foundation Models with Multimodal Inputs
arXiv cs.LG · 24 July 2026
DOD selects Accenture to investigate ‘existential supply chain vulnerability’ threatening military medicine
DefenseScoop · 17 July 2026
How to cite this record
ethics.ai (14 April 2026), “Identifying and Mitigating Gender Cues in Academic Recommendation Letters: An Interpretability Case Study,” evidence record 5891, https://ethics.ai/record/5891 (originally published by arXiv).
Use and limitations
This page is a stable index and citation surface for a source record. ethics.ai did not author the underlying report and has not independently verified every claim. Automatic topics may be imperfect. For consequential use, quote and cite the original publisher.