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Many datasets contain personally identifiable information, or PII, whichposes privacy risks to individuals. PII masking is commonly used to redactpersonal information such as names, addresses, and phone numbers from textdata. Most modern PII masking pipelines involve machine learning algorithms.However, these systems may vary in performance, such that individuals fromparticular demographic groups bear a higher risk for having their personalinformation exposed. In this paper, we evaluate the performance of threeoff-the-shelf PII masking systems on name detection and redaction. We generatedata using names and templates from the customer service domain. We find thatan open-source RoBERTa-based system shows fewer disparities than the commercialmodels we test. However, all systems demonstrate significant differences inerror rate based on demographics. In particular, the highest error ratesoccurred for names associated with Black and Asian/Pacific Islanderindividuals.

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