no code implementations • 20 Jun 2023 • Kenya S. Andrews, Bhuvani Shah, Lu Cheng
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.