There have been growing concerns around high-stake applications For decades, medicine has been marred by implicit and explicit that rely on models trained with biased data, which consequently biases that continue to negatively impact patient outcomes by perpetuating produce biased predictions, often harming the most vulnerable. In stereotypes and contributing to health disparities among particular, biased medical data could cause health-related applications social groups that face systemic oppression [8, 9]. Despite efforts to and recommender systems to create outputs that jeopardize remediate and address these biases from their source, many medical patient care and widen disparities in health outcomes. A recent schools still incorporate biased medical teachings during the framework titled Fairness via AI posits that, instead of attempting preclinical years [12, 28]. Many educators continue to misuse race to correct model biases, researchers must focus on their root causes as a substitute for genetics or ancestry, or they use gender and sex by using AI to debias data. Inspired by this framework, we tackle terms incorrectly reinforcing the notion that sex and gender are bias detection in medical curricula using NLP models, including binary or fixed rather than fluid, which can potentially alienate LLMs, and evaluate them on a gold standard dataset containing gender-nonconforming students and patients [1, 14, 15]. The current 4,105 excerpts annotated by medical experts for bias from a large focus in AI research is primarily on identifying and exposing corpus. We build on previous work by coauthors which augments bias within AI systems, often without addressing the root causes the set of negative samples with non-annotated text containing social of bias inherent in the data these systems are built upon.

Biased information (recently termed bisinformation) continues to be taught in medical curricula, often long after having been debunked. In this paper, we introduce BRICC, a firstin-class initiative that seeks to mitigate medical bisinformation using machine learning to systematically identify and flag text with potential biases, for subsequent review in an expert-in-the-loop fashion, thus greatly accelerating an otherwise labor-intensive process. A gold-standard BRICC dataset was developed throughout several years, and contains over 12K pages of instructional materials. Medical experts meticulously annotated these documents for bias according to comprehensive coding guidelines, emphasizing gender, sex, age, geography, ethnicity, and race. Using this labeled dataset, we trained, validated, and tested medical bias classifiers. We test three classifier approaches: a binary type-specific classifier, a general bias classifier; an ensemble combining bias type-specific classifiers independently-trained; and a multitask learning (MTL) model tasked with predicting both general and type-specific biases. While MTL led to some improvement on race bias detection in terms of F1-score, it did not outperform binary classifiers trained specifically on each task. On general bias detection, the binary classifier achieves up to 0.923 of AUC, a 27.8% improvement over the baseline. This work lays the foundations for debiasing medical curricula by exploring a novel dataset and evaluating different training model strategies. Hence, it offers new pathways for more nuanced and effective mitigation of bisinformation.