Gender Bias in Big Data Analysis.
This article combines humanistic "data critique" with informed inspection of big data analysis. It measures gender bias when gender prediction software tools (Gender API, Namsor, and Genderize.io) are used in historical big data research. Gender bias is measured by contrasting personally identified...
| Published in: | Information & Culture Vol. 57; no. 3; pp. 1 - 26 |
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| Format: | Article |
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University of Texas Press
2022
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=160117630&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 160117630 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 21648034 FB2C jtl: Information & Culture issn: 21648034 maglogo: N pubinfo: dt: 2022 vid: 57 iid: 3 pid: 519 pub: University of Texas Press artinfo: ui: 160117630 10.7560/ic57303 ppf: 1 ppct: 25 formats: fmt: – @attributes: type: T – @attributes: type: P size: 784KB tig: atl: Gender Bias in Big Data Analysis. aug: au: Misa, Thomas J. su: Big data Data analysis Software development tools Computer science Gender Sex discrimination sug: subj: Big data Data analysis Software development tools Computer science Gender Sex discrimination keyword: algorithmic bias big data computer science research digital humanities gender bias history of computing ab: This article combines humanistic "data critique" with informed inspection of big data analysis. It measures gender bias when gender prediction software tools (Gender API, Namsor, and Genderize.io) are used in historical big data research. Gender bias is measured by contrasting personally identified computer science authors in the well-regarded DBLP dataset (1950–80) with exactly comparable results from the software tools. Implications for public understanding of gender bias in computing and the nature of the computing profession are outlined. Preliminary assessment of the Semantic Scholar dataset is presented. The conclusion combines humanistic approaches with selective use of big data methods. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Copyright of Information & Culture is the property of University of Texas Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. item: Information & Culture holder: University of Texas Press dt: @attributes: year: 2022 holdings: @attributes: islocal: N |
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