Editing Digital divide (section)

===Gender gap===
{{Main|Gender digital divide}}

Due to the rapidly declining price of connectivity and hardware, skills deficits have eclipsed barriers of access as the primary contributor to the [[gender digital divide]]. Studies show that women are less likely to know how to leverage devices and Internet access to their full potential, even when they do use digital technologies.<ref name=":5">{{Cite web|date=2019|title=I'd blush if I could: closing gender divides in digital skills through education|url=https://unesdoc.unesco.org/ark:/48223/pf0000367416.pdf|publisher=UNESCO, EQUALS Skills Coalition|access-date=March 4, 2020|archive-date=March 30, 2020|archive-url=https://web.archive.org/web/20200330231252/https://unesdoc.unesco.org/ark:/48223/pf0000367416.pdf|url-status=live}}</ref> In rural [[India]], for example, a study found that the majority of women who owned [[mobile phone]]s only knew how to answer calls. They could not dial numbers or read messages without assistance from their husbands, due to a lack of literacy and numeracy skills.<ref>Mariscal, J., Mayne, G., Aneja, U. and Sorgner, A. 2018. Bridging the Gender Digital Gap. Buenos Aires, CARI/CIPPEC.</ref> A survey of 3,000 respondents across 25 countries found that adolescent boys with [[mobile phone]]s used them for a wider range of activities, such as playing games and accessing financial services online. Adolescent girls in the same study tended to use just the basic functionalities of their phone, such as making calls and using the calculator.<ref name="Vodafone Foundation 2018">Girl Effect and Vodafone Foundation. 2018. Real Girls, Real Lives, Connected. London, Girl Effect and Vodafone Foundation.</ref> Similar trends can be seen even in areas where Internet access is near-universal. A survey of women in nine cities around the world revealed that although 97% of women were using social media, only 48% of them were expanding their networks, and only 21% of Internet-connected women had searched online for information related to health, legal rights or transport.<ref name="Vodafone Foundation 2018" /> In some cities, less than one quarter of connected women had used the Internet to look for a job.<ref name=":5" />

[[File:Abilities and perceptions of abilities.svg|thumb|Abilities and perceptions of abilities]]

Studies show that despite strong performance in computer and information literacy (CIL), girls do not have confidence in their [[Information and communications technology|ICT]] abilities. According to the [[International Computer and Information Literacy Study]] (ICILS) assessment girls' [[self-efficacy]] scores (their perceived as opposed to their actual abilities) for advanced ICT tasks were lower than boys'.<ref>Fjeld, A. 2018. AI: A Consumer Perspective. March 13, 2018. New York, LivePerson.</ref><ref name=":5" />

A paper published by J. Cooper from Princeton University points out that learning technology is designed to be receptive to men instead of women. Overall, the study presents the problem of various perspectives in society that are a result of gendered socialization patterns that believe that computers are a part of the male experience since computers have traditionally presented as a toy for boys when they are children.<ref>{{cite journal |doi=10.1111/j.1365-2729.2006.00185.x |title=The digital divide: The special case of gender |year=2006 |last1=Cooper |first1=J. |journal=Journal of Computer Assisted Learning |volume=22 |issue=5 |pages=320–334 }}</ref> This divide is followed as children grow older and young girls are not encouraged as much to pursue degrees in IT and computer science. In 1990, the percentage of women in computing jobs was 36%, however in 2016, this number had fallen to 25%. This can be seen in the under representation of women in IT hubs such as Silicon Valley.<ref>{{cite web |last1=Mundy |first1=Liza |title=Why Is Silicon Valley So Awful to Women? |url=https://www.theatlantic.com/magazine/archive/2017/04/why-is-silicon-valley-so-awful-to-women/517788/ |website=The Atlantic |access-date=April 17, 2020 |date=April 2017 |archive-date=January 26, 2021 |archive-url=https://web.archive.org/web/20210126122251/https://www.theatlantic.com/magazine/archive/2017/04/why-is-silicon-valley-so-awful-to-women/517788/ |url-status=live }}</ref>

There has also been the presence of algorithmic bias that has been shown in machine learning algorithms that are implemented by major companies.{{clarify |date=March 2020 |reason="Major companies", plural, is claimed, but only a single company is mentioned and referenced.}} In 2015, Amazon had to abandon a recruiting algorithm that showed a difference between ratings that candidates received for software developer jobs as well as other technical jobs. As a result, it was revealed that Amazon's machine algorithm was biased against women and favored male resumes over female resumes. This was due to the fact that Amazon's computer models were trained to vet patterns in resumes over a 10-year period. During this ten-year period, the majority of the resumes belong to male individuals, which is a reflection of male dominance across the tech industry.<ref>{{cite news |last1=Dastin |first1=Jeffrey |title=Amazon scraps secret AI recruiting tool that showed bias against women |url=https://www.reuters.com/article/us-amazon-com-jobs-automation-insight/amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK08G |work=Reuters |date=10 October 2018 |access-date=December 11, 2019 |archive-date=December 12, 2019 |archive-url=https://web.archive.org/web/20191212195222/https://www.reuters.com/article/us-amazon-com-jobs-automation-insight/amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK08G |url-status=live }}</ref>