The unseen Black faces of AI algorithms

Abeba Birhane ()

Nature, 2022, vol. 610, issue 7932, 451-452

Abstract: An audit of commercial facial-analysis tools found that dark-skinned faces are misclassified at a much higher rate than are faces from any other group. Four years on, the study is shaping research, regulation and commercial practices.

Keywords: Computer science; Ethics; Information technology; Machine learning (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/d41586-022-03050-7 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:nature:v:610:y:2022:i:7932:d:10.1038_d41586-022-03050-7

Ordering information: This journal article can be ordered from
https://www.nature.com/

DOI: 10.1038/d41586-022-03050-7

Access Statistics for this article

Nature is currently edited by Magdalena Skipper

More articles in Nature from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().