用因果模型分析医疗记录中的偏见，小改动提升患者叙述可信度

In the context of medical records, patients often experience testimonial injustice, where the textual account undermines the validity of their experiences. Past work has demonstrated that intersectionality of demographic features is crucial to \emph{detect} such injustice. We use causal discovery to study the degree to which certain demographic features tied to marginalization (namely age, gender, and race), together lead to specific types of testimonial injustice terms. This offers us an insightful Structural Causal Model (SCM) relating those demographic features to the different ways a patients' reality may be undermined. We then move toward \emph{addressing} such injustice, by very selectively (based on insights from the SCM) editing physicians' notes. For comparison, we contrast these rule-based edits with blanket context-based modifications using an LLM. We assess the impact of these changes on the perception of patients' experiences, using human experts and an LLM. We find that edits, in general, enhance clarity regarding the urgency and causes of patients' conditions. Additionally, for human experts, rule-based modifications show a tendency to shift blame away from patients, and toward more objective external factors. These findings (1) underscore the importance of quantifying sources of injustice in how patients' testimonies are recorded, (2) reveal that making minimal intentional changes accordingly can effect improved patient perception, and (3) call for larger efforts to assess health outcomes under such more just representation.