Equal accuracy for Andrew and Abubakar—detecting and mitigating bias in name-ethnicity classification algorithms

Abstract

Uncovering the world’s ethnic inequalities is hampered by a lack of ethnicity-annotated datasets. Name-ethnicity classifiers (NECs) can help, as they are able to infer people’s ethnicities from their names. However, since the latest generation of NECs rely on machine learning and artificial intelligence (AI), they may suffer from the same racist and sexist biases found in many AIs. Therefore, this paper offers an algorithmic fairness audit of three NECs. It finds that the UK-Census-trained EthnicityEstimator displays large accuracy biases with regards to ethnicity, but relatively less among gender and age groups. In contrast, the Twitter-trained NamePrism and the Wikipedia-trained Ethnicolr are more balanced among ethnicity, but less among gender and age. We relate these biases to global power structures manifested in naming conventions and NECs’ input distribution of names. To improve on the uncovered biases, we program a novel NEC, N2E, using fairness-aware AI techniques. We make N2E freely available at www.name-to-ethnicity.com.

A Century of Evolution in Spine Surgery Publications: A Bibliometric Analysis of the Field From 1900 to 2023

“…Separating noise from signal and manually verifying the quality of each datapoint is impossible; as a result, we relied on validated computational tools to infer trends in publications and author characteristics, while remaining cognizant of the inherent biases or inaccuracies that these tools may embody. 57…”

“…Separating noise from signal and manually verifying the quality of each datapoint is impossible; as a result, we relied on validated computational tools to infer trends in publications and author characteristics, while remaining cognizant of the inherent biases or inaccuracies that these tools may embody. 57 In particular, the inference of race, ethnicity, and sex based on names is an approach commonly applied in the social sciences when annotated data are not available because of logistical or discriminatory concerns. 35,58,59 Although such algorithms merely provide a probability that a name is associated with a particular identity group, the general trends we identified highlight crucial deficits in representation.…”

Artificial Intelligence (AI) Reveals Ethnic Disparities in Cataract Detection and Treatment

“…To meet the needs of the present study, the original NEC machine learning model proposed by Hafner et al [ 9 ] was customized. The original model analyzes the individual characters of names and classifies them into up to 49 nationalities and ethnic groups.…”

“…The training was performed following the methodology described by Hafner et al [ 9 ]. The remaining 10% of the dataset, i.e., 17,408 names, were then used to evaluate the model, resulting in an 88.09% accuracy score.…”

“…However, recent innovations in artificial intelligence have introduced new data analyzing techniques. Name-ethnicity classifiers (NECs) are capable of inferring people’s ethnicities from their names with an accuracy of over 88%, allowing researchers to investigate potential ethnic disparities in datasets where this was previously impossible due to data limitations [ 9 ]. Additionally, we believe that Austria, with its multiethnic society, could serve as a role model for detecting and improving ethical inequalities in other countries.…”