  Data sharing is crucial for open science and reproducible research, but the
legal sharing of clinical data requires the removal of protected health
information from electronic health records. This process, known as
de-identification, is often achieved through the use of machine learning
algorithms by many commercial and open-source systems. While these systems have
shown compelling results on average, the variation in their performance across
different demographic groups has not been thoroughly examined. In this work, we
investigate the bias of de-identification systems on names in clinical notes
via a large-scale empirical analysis. To achieve this, we create 16 name sets
that vary along four demographic dimensions: gender, race, name popularity, and
the decade of popularity. We insert these names into 100 manually curated
clinical templates and evaluate the performance of nine public and private
de-identification methods. Our findings reveal that there are statistically
significant performance gaps along a majority of the demographic dimensions in
most methods. We further illustrate that de-identification quality is affected
by polysemy in names, gender context, and clinical note characteristics. To
mitigate the identified gaps, we propose a simple and method-agnostic solution
by fine-tuning de-identification methods with clinical context and diverse
names. Overall, it is imperative to address the bias in existing methods
immediately so that downstream stakeholders can build high-quality systems to
serve all demographic parties fairly.

Machine Learning;Computation and Language;Cryptography and Security;Computers and Society