Towards Fairer Health Recommendations: finding informative unbiased samples via Word Sense Disambiguation

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.