{"id":72539,"date":"2022-07-17T09:41:00","date_gmt":"2022-07-17T13:41:00","guid":{"rendered":"https:\/\/www.shortform.com\/blog\/?p=72539"},"modified":"2022-07-21T11:07:55","modified_gmt":"2022-07-21T15:07:55","slug":"invisible-women-data-bias","status":"publish","type":"post","link":"https:\/\/www.shortform.com\/blog\/invisible-women-data-bias\/","title":{"rendered":"Invisible Women: Data Bias Works in Men’s Favor"},"content":{"rendered":"\n

How does Invisible Women<\/em> define data bias? How does data bias hurt women on a healthcare and safety level?<\/p>\n\n\n\n

As explained in Invisible Women<\/em>, data bias occurs when socio-cultural prejudices affect systematic processes. The author, Caroline Criado Perez, specifically talks about how a lack of information about the female experience affects women’s health, safety, and economic standing.<\/p>\n\n\n\n

Continue reading to learn more about gender data bias.<\/p>\n\n\n\n\n\n\n\n

Invisible Women<\/em>‘s Data Bias Critique<\/strong><\/h2>\n\n\n\n

When you read the words \u201caverage person,\u201d who do you picture? If you\u2019re like most people, you probably picture a man. In Invisible Women<\/em>, feminist campaigner Caroline Criado Perez argues that this is because most humans operate under a male-as-default mindset: We consider the average person to be male<\/em>. This mindset is particularly visible in gendered languages. For example, in Spanish, the masculine el doctor <\/em>technically means \u201cmale doctor.\u201d However, it is often used to refer to a doctor of any gender. <\/p>\n\n\n\n

(Shortform note: Perez notes that gendered languages often default to the male forms of words, but some speakers of these languages are pushing back against these gendered words<\/a>\u2014which suggests that the male-as-default mindset may be waning. For example, some Spanish speakers use gender-neutral endings to words, like –e<\/em> or –x<\/em>.)<\/p>\n\n\n\n

In Invisible Women<\/em>, data bias is said to be caused by this male-as-default mindset. Perez contends that <\/strong>this also causes a lack of information about the female experience. This is because we don\u2019t consider women the \u201caverage person,\u201d so we don\u2019t collect data on them. <\/p>\n\n\n\n

(Shortform note: Although Perez didn\u2019t coin the term \u201cgender data gap,\u201d data suggests that she did introduce it to the public consciousness. Google searches for \u201cgender data gap\u201d<\/a> remained relatively low for years but spiked in February 2019. They have remained relatively high since then. Given that Invisible Women<\/em> was published in March 2019, this spike appears to be related to the book\u2019s popularity. The top related queries to the term also include both the author and the book\u2019s name.) <\/p>\n\n\n\n

In turn, this gender data gap reinforces the male-as-default mindset. <\/strong>When there is no data about women, people (especially men) assume that the experience of the average man represents the experience of the average woman. Given this, they continue to make choices that reflect this mindset. But ultimately it harms women. In this way, the male-as-default mindset and the gender data gap create a vicious, mutually reinforcing cycle.<\/p>\n\n\n\n

How the Gender Data Gap Reinforces Itself<\/strong><\/h3>\n\n\n\n

It\u2019s not always the male-as-default mindset that causes or is reinforced by the gender data gap. Sometimes, one gender data gap sets off a self-reinforcing cycle or engenders another gender data gap. One of the world\u2019s most popular websites, Wikipedia is ostensibly a gender-neutral encyclopedia. But further analysis demonstrates that it suffers from a gender data gap problem. Wikipedia is open source, meaning that anybody can edit it\u2014but most of its editors are white American men<\/a>. <\/p>\n\n\n\n

Because these men don\u2019t have firsthand information about the female experience, Wikipedia\u2019s editing team suffers from a gender data gap. As a result, it makes decisions that discourage women from joining this team<\/a>\u2014like calling rape scenes in movies \u201csex scenes\u201d because the term rape was \u201cnot neutral.\u201d In this way, having mostly men on the editing team ensures that mostly men remain on the editing team\u2014in other words, the gender data gap reinforces itself. And this lack of women on the editing team can be harmful to women\u2019s health and safety. Several women on the editing team have been subject to harassment from other editors. <\/p>\n\n\n\n

But that\u2019s not all. Critics suggest that the lack of women on the editing team explains why Wikipedia\u2019s data is skewed: Only 18% of the content on Wikipedia is about women<\/a>, and articles about women are far more likely to be deleted<\/a> despite meeting the company\u2019s standards regarding whether they\u2019re deserving of a Wikipedia entry. In this way, the gender data gap on the editing team engenders a gender data gap in which Wikipedia has insufficient information on women. And this may harm women\u2019s economic standing, given that someone\u2019s recognizability is often relevant to how much they\u2019re paid, and having a Wikipedia page is an easy way to demonstrate that they\u2019re a semi-public figure.<\/p>\n","protected":false},"excerpt":{"rendered":"

How does Invisible Women define data bias? How does data bias hurt women on a healthcare and safety level? As explained in Invisible Women, data bias occurs when socio-cultural prejudices affect systematic processes. The author, Caroline Criado Perez, specifically talks about how a lack of information about the female experience affects women’s health, safety, and economic standing. Continue reading to learn more about gender data bias.<\/p>\n","protected":false},"author":14,"featured_media":69953,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[40,24,25],"tags":[687],"class_list":["post-72539","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-books","category-society","category-statistics","tag-invisible-women","","tg-column-two"],"yoast_head":"\nInvisible Women: Data Bias Works in Men's Favor - Shortform Books<\/title>\n<meta name=\"description\" content=\"How does Caroline Criado Perez define gender data bias in Invisible Women? 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