Evidence map›Paper›PMID 36789242›Full record

ArticleAI & society2023

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

Lena Hafner, Theodor Peter Peifer, Franziska Sofia Hafner

Abstract read
In one paragraph

Article in AI & society, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. The Evolution of Skull Base Surgery: A Bibliometric Analysis Spanning Nearly 125 Years.Journal of neurological surgery. Part B, Skull base · 2025
    Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Lena HafnerDepartment of Politics and International Studies, University of Cambridge, Cambridge, UK.ORCID 0000-0001-8264-3031
Theodor Peter PeiferMunich Center for Digital Sciences and AI, Hochschule München, Munich, Germany.ORCID 0000-0003-3388-3869
Franziska Sofia HafnerDepartment of Social and Public Policy and Computer Science, University of Glasgow, Glasgow, UK.ORCID 0000-0003-1070-8267

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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 Supplementary Information: The online version contains supplementary material available at 10.1007/s00146-022-01619-4.

Indexed as

AI fairness auditAlgorithmic biasArtificial intelligenceCritical techEthnic inequalitiesMachine learningName-ethnicity-classification

Identifiers

PMID36789242
PMCPMC9910274

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.