AI can be sexist and racist — it’s time to make it fair

James Zou () and Londa Schiebinger ()

Nature, 2018, vol. 559, issue 7714, 324-326

Abstract: Computer scientists must identify sources of bias, de-bias training data and develop artificial-intelligence algorithms that are robust to skews in the data, argue James Zou and Londa Schiebinger.

Keywords: Information technology; Society (search for similar items in EconPapers)
Date: 2018
References: Add references at CitEc
Citations: View citations in EconPapers (18)

Downloads: (external link)
https://www.nature.com/articles/d41586-018-05707-8 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:559:y:2018:i:7714:d:10.1038_d41586-018-05707-8

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

DOI: 10.1038/d41586-018-05707-8

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 ().