Behind the Mask: Demographic bias in name detection for PII masking

Kristen Howell

Abstract:

Many datasets contain personally identifiable
information, or PII, which poses privacy risks
to individuals. PII masking is commonly used
to redact personal information such as names,
addresses, and phone numbers from text data.
Most modern PII masking pipelines involve
machine learning algorithms. However, these
systems may vary in performance, such that individuals from particular demographic groups
bear a higher risk for having their personal information exposed. In this paper, we evaluate
the performance of three off-the-shelf PII masking systems on name detection and redaction.
We generate data using names and templates
from the customer service domain. We find
that an open-source RoBERTa-based system
shows fewer disparities than the commercial
models we test. However, all systems demonstrate significant differences in error rate based
on demographics. In particular, the highest error rates occurred for names associated with
Black and Asian/Pacific Islander individuals.