Incorporating Human Explanations for Robust Hate Speech Detection

Given the black-box nature and complexity of large transformer language models (LM), concerns about generalizability and robustness present ethical implications for domains such as hate speech (HS) detection. Using the content rich Social Bias Frames dataset, containing human-annotated stereotypes, intent, and targeted groups, we develop a three stage analysis to evaluate if LMs faithfully assess hate speech. First, we observe the need for modeling contextually grounded stereotype intents to capture implicit semantic meaning. Next, we design a new task, Stereotype Intent Entailment (SIE), which encourages a model to contextually understand stereotype presence. Finally, through ablation tests and user studies, we find a SIE objective improves content understanding, but challenges remain in modeling implicit intent.

Exploring Gender Disparities in Time to Diagnosis

Sex and gender-based healthcare disparities contribute to differences in health outcomes. We focus on time to diagnosis (TTD) by conducting two large-scale, complementary analyses among men and women across 29 phenotypes and 195K patients. We first find that women are consistently more likely to experience a longer TTD than men, even when presenting with the same conditions. We further explore how TTD disparities affect diagnostic performance between genders, both across and persistent to time, by evaluating gender-agnostic disease classifiers across increasing diagnostic information. In both fairness analyses, the diagnostic process favors men over women, contradicting the previous observation that women may demonstrate relevant symptoms earlier than men. These analyses suggest that TTD is an important yet complex aspect when studying gender disparities, and warrants further investigation.