We address two emerging concerns in algorithmic fairness: (i) redundant encodings of race – the notion that machine learning models encode race with probability nearing one as the feature set grows – which is widely noted in theory, with little empirical evidence; and (ii) the lack of race and ethnicity data in many domains, where state-of-the-art remains (Naive) Bayesian Improved Surname Geocoding (BISG) that relies on name and geographic information. We leverage a novel and highly granular dataset of over 7.7 million patients’ electronic health records to provide one of the first empirical studies of redundant encodings in a realistic health care setting and examine the ability to assess health care disparities when race
may be missing.

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