Summary of See Me and Believe Me: Causality and Intersectionality in Testimonial Injustice in Healthcare, by Kenya S. Andrews et al.
See Me and Believe Me: Causality and Intersectionality in Testimonial Injustice in Healthcare
by Kenya S. Andrews, Mesrob I. Ohannessian, Elena Zheleva
First submitted to arxiv on: 2 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper tackles testimonial injustice in medical settings, where patients’ voices are often misheard or marginalized due to prejudices. The authors propose a novel approach using causal discovery methods to quantify the impact of demographic features (age, gender, race) on testimonial injustice. They analyze physicians’ notes to identify unjust vocabulary and build Structural Causal Models (SCMs) to illustrate how these factors interact. The results show that intersectionality is crucial in understanding biased experiences, as a single feature can make individuals more prone to experiencing another form of injustice. This work paves the way for using causal discovery to improve patient care by designing healthcare systems that promote trust and equality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how doctors’ notes can be unfair because of biases. When people have different characteristics, like age or gender, they might not be heard correctly in medical settings. The researchers used a special method called FCI (causal discovery) to study how these differences affect the way patients are treated unfairly. They analyzed doctor’s notes to find words that are unjust and built models to show how these factors work together. The results showed that it’s important to consider multiple factors, like age and gender, when understanding why some people might be treated unfairly. This is an important step in making healthcare more fair and trustworthy. |
