  Detecting testimonial injustice is an essential element of addressing
inequities and promoting inclusive healthcare practices, many of which are
life-critical. However, using a single demographic factor to detect testimonial
injustice does not fully encompass the nuanced identities that contribute to a
patient's experience. Further, some injustices may only be evident when
examining the nuances that arise through the lens of intersectionality.
Ignoring such injustices can result in poor quality of care or life-endangering
events. Thus, considering intersectionality could result in more accurate
classifications and just decisions. To illustrate this, we use real-world
medical data to determine whether medical records exhibit words that could lead
to testimonial injustice, employ fairness metrics (e.g. demographic parity,
differential intersectional fairness, and subgroup fairness) to assess the
severity to which subgroups are experiencing testimonial injustice, and analyze
how the intersectionality of demographic features (e.g. gender and race) make a
difference in uncovering testimonial injustice. From our analysis, we found
that with intersectionality we can better see disparities in how subgroups are
treated and there are differences in how someone is treated based on the
intersection of their demographic attributes. This has not been previously
studied in clinical records, nor has it been proven through empirical study.

Computers and Society;Machine Learning