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See Me and Believe Me: Causality and Intersectionality in Testimonial Injustice in Healthcare

Kenya S. Andrews, Mesrob I. Ohannessian, Elena Zheleva

TL;DR

This work uses FCI, a causal discovery method, to study the degree to which certain demographic features could lead to marginalization by way of contributing to testimonial injustice, and observes how one contributing feature can make a person more prone to experiencing another contributor of testimonial injustice.

Abstract

In medical settings, it is critical that all who are in need of care are correctly heard and understood. When this is not the case due to prejudices a listener has, the speaker is experiencing \emph{testimonial injustice}, which, building upon recent work, we quantify by the presence of several categories of unjust vocabulary in medical notes. In this paper, we use FCI, a causal discovery method, to study the degree to which certain demographic features could lead to marginalization (e.g., age, gender, and race) by way of contributing to testimonial injustice. To achieve this, we review physicians' notes for each patient, where we identify occurrences of unjust vocabulary, along with the demographic features present, and use causal discovery to build a Structural Causal Model (SCM) relating those demographic features to testimonial injustice. We analyze and discuss the resulting SCMs to show the interaction of these factors and how they influence the experience of injustice. Despite the potential presence of some confounding variables, we observe how one contributing feature can make a person more prone to experiencing another contributor of testimonial injustice. There is no single root of injustice and thus intersectionality cannot be ignored. These results call for considering more than singular or equalized attributes of who a person is when analyzing and improving their experiences of bias and injustice. This work is thus a first foray at using causal discovery to understand the nuanced experiences of patients in medical settings, and its insights could be used to guide design principles throughout healthcare, to build trust and promote better patient care.

See Me and Believe Me: Causality and Intersectionality in Testimonial Injustice in Healthcare

TL;DR

This work uses FCI, a causal discovery method, to study the degree to which certain demographic features could lead to marginalization by way of contributing to testimonial injustice, and observes how one contributing feature can make a person more prone to experiencing another contributor of testimonial injustice.

Abstract

In medical settings, it is critical that all who are in need of care are correctly heard and understood. When this is not the case due to prejudices a listener has, the speaker is experiencing \emph{testimonial injustice}, which, building upon recent work, we quantify by the presence of several categories of unjust vocabulary in medical notes. In this paper, we use FCI, a causal discovery method, to study the degree to which certain demographic features could lead to marginalization (e.g., age, gender, and race) by way of contributing to testimonial injustice. To achieve this, we review physicians' notes for each patient, where we identify occurrences of unjust vocabulary, along with the demographic features present, and use causal discovery to build a Structural Causal Model (SCM) relating those demographic features to testimonial injustice. We analyze and discuss the resulting SCMs to show the interaction of these factors and how they influence the experience of injustice. Despite the potential presence of some confounding variables, we observe how one contributing feature can make a person more prone to experiencing another contributor of testimonial injustice. There is no single root of injustice and thus intersectionality cannot be ignored. These results call for considering more than singular or equalized attributes of who a person is when analyzing and improving their experiences of bias and injustice. This work is thus a first foray at using causal discovery to understand the nuanced experiences of patients in medical settings, and its insights could be used to guide design principles throughout healthcare, to build trust and promote better patient care.
Paper Structure (21 sections, 4 figures, 2 tables)

This paper contains 21 sections, 4 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Works
  3. Problem Description
  4. Assumptions
  5. Do you see me?
  6. Do you believe me?
  7. No interference between patients or physicians
  8. Algorithm-specific assumptions
  9. Data
  10. MIMIC-III
  11. Testimonial Injustice Lexicon
  12. Methodology
  13. Lexicon Lookup
  14. Aggregating Notes
  15. Instances of Injustice
  16. ...and 6 more sections

Figures (4)

  • Figure 1: FCI SCMs with the minimum $\alpha$-value that connects (a) 1 demographic feature, (b) 2 demographic features, and (c) 3 demographic features.
  • Figure 2: FCI SCMs using doubled data with the minimum $\alpha$-value that connects (a) 1 demographic feature, (b) 2 demographic features, and (c) 3 demographic features.
  • Figure 3: FCI SCM with coarse granularity and $\alpha$ = 0.05
  • Figure 4: PC SCMs with the minimum $\alpha$-value that connects (a) 1 demographic feature, (b) 2 demographic features, and (c) 3 demographic features.