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Name-ethnicity classifiers (NECs) can help, as they are able to infer people\u2019s 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 <jats:italic>EthnicityEstimator<\/jats:italic> displays large accuracy biases with regards to ethnicity, but relatively less among gender and age groups. In contrast, the Twitter-trained <jats:italic>NamePrism<\/jats:italic> and the Wikipedia-trained <jats:italic>Ethnicolr<\/jats:italic> 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\u2019 input distribution of names. To improve on the uncovered biases, we program a novel NEC, <jats:italic>N2E<\/jats:italic>, using fairness-aware AI techniques. We make <jats:italic>N2E<\/jats:italic> freely available at <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"http:\/\/www.name-to-ethnicity.com\">www.name-to-ethnicity.com<\/jats:ext-link>.\n<\/jats:p>","DOI":"10.1007\/s00146-022-01619-4","type":"journal-article","created":{"date-parts":[[2023,2,10]],"date-time":"2023-02-10T02:14:13Z","timestamp":1675995253000},"page":"1605-1629","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Equal accuracy for Andrew and Abubakar\u2014detecting and mitigating bias in name-ethnicity classification algorithms"],"prefix":"10.1007","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8264-3031","authenticated-orcid":false,"given":"Lena","family":"Hafner","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3388-3869","authenticated-orcid":false,"given":"Theodor Peter","family":"Peifer","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1070-8267","authenticated-orcid":false,"given":"Franziska Sofia","family":"Hafner","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,2,9]]},"reference":[{"key":"1619_CR1","doi-asserted-by":"publisher","first-page":"584","DOI":"10.1007\/978-3-030-13469-3_68","volume":"11401","author":"A Acien","year":"2019","unstructured":"Acien A (2019) Measuring the gender and ethnicity bias in deep models for face recognition. 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