{"id":93033,"date":"2019-09-13T06:52:21","date_gmt":"2019-09-13T04:52:21","guid":{"rendered":"https:\/\/uniavisen.dk\/sexede-kvinder-og-modige-maend-koensstereotyper-i-litteraturen-paavirker-kunstig-intelligens\/"},"modified":"2019-09-13T11:51:15","modified_gmt":"2019-09-13T09:51:15","slug":"sexy-women-and-brave-men-gender-stereotypes-in-literature-are-affecting-artificial-intelligence","status":"publish","type":"post","link":"https:\/\/uniavisen.dk\/en\/sexy-women-and-brave-men-gender-stereotypes-in-literature-are-affecting-artificial-intelligence\/","title":{"rendered":"Sexy women and brave men: Gender stereotypes in literature are affecting artificial intelligence"},"content":{"rendered":"

In collaboration with an international research group, computer scientist at the University of Copenhagen Isabelle Augenstein<\/secret> has studied language use in fiction and academic literature from 1900 to 2008. Using machine learning, researchers have identified the adjectives and verbs associated with nouns in 3.5 million books.<\/p>\n

We do not just present the research results in itself, but also a general method on how to trace gender-biased language in larger collections of text<\/p>\n

Isabelle Augenstein, assistant professor at the Department of Computer Science<\/p>\n<\/blockquote>\n

The group’s research shows that there are significant differences in the literature on how female nouns (e.g. daughter, mother, princess, queen) and male nouns (father, son, brother, husband) are described with adjectives.<\/p>\n

The female gendered nouns are described with adjectives associated with their appearance, while men are described with words associated with their behaviour. While literature’s women are beautiful or sexy, men are primarily righteous and courageous.<\/p>\n

In sociolinguistics, researchers have for a long time been interested in the extent to which our language is gender specific. However, the computer scientists\u2019 latest research results have been achieved with new quantitative methods.<\/p>\n

\u00bbWe do not just present the research results by themselves, but also as a general method to trace gender-biased language in larger collections of text. A method that can be used on other data sets,\u00ab Isabella Augenstein writes in an email to the University Post.<\/p>\n

Via machine learning, the computer scientists have used a huge data set<\/secret>, and they have been able to determine the extent of gendered language and whether the adjectives and verbs are positive or negative.<\/p>\n

Affects today\u2019s society<\/h3>\n

Although the researchers have not used literature written after 2008, the gendered language of older texts is relevant for today: The algorithm used in programmes and machines that recognise people’s voices largely use texts available on the internet \u2013 including literary works and articles from many years ago. This goes for when smartphones recognise voices, or when Google prompts keywords.<\/p>\n

\u00bbA commonly used data set is, for example, the New York Times\u2019 data set, which contains articles from the period 1987-2007,\u00ab writes Isabelle Augenstein.<\/p>\n

The machines build up their ‘truth’ based on older texts that make use of gender stereotypes.<\/p>\n

\n

Read more<\/p>\n

The research article on the project, which was recently presented at the conference ACL 2019, can be found here.<\/a><\/p>\n

 <\/p>\n<\/div>\n

\u00bbIn addition, I would argue that people still engage with older literature\u00ab. Older, written, language influences our society in the present: \u00bbThink of the classic children’s books like the Brothers Grimm\u2019s fairy tales or recent film adaptations of historical dramas,\u00ab writes Isabelle Augenstein.<\/p>\n

To follow up on the results, the research group which Isabelle Augenstein is a part of has just started a new study. It will be able to say more about how gender stereotypes have unfolded historically. They do this by taking the newly presented research results and dividing them into decades.<\/p>\n

\u00bbBased on what other researchers have found, we expect to be able to see that there are still gender stereotypes in modern books, even though they may be couched in a slightly different language,\u00ab she writes.
\n<\/p>\n","protected":false},"excerpt":{"rendered":"

In literature, men are courageous, rational, and commit acts of violence. Women are typically sexy, pretty and fertile. The gender norms in the books influence how the machines of today learn human language.<\/p>\n","protected":false},"author":70,"featured_media":92830,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"om_disable_all_campaigns":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"_uf_show_specific_survey":0,"_uf_disable_surveys":false,"footnotes":""},"categories":[46],"tags":[741,926,2307,2308,2277,2311,2312,2313],"class_list":["post-93033","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-science","tag-computer-science","tag-gender-studies","tag-isabelle-augenstein-en","tag-kunstig-intelligens-en","tag-koen-en","tag-maskinlaering-en","tag-navneord-en","tag-tillaegsord-en","expression-news_article"],"acf":[],"aioseo_notices":[],"yoast_head":"\nSexy women and brave men: Gender stereotypes in literature are affecting artificial intelligence<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/uniavisen.dk\/en\/sexy-women-and-brave-men-gender-stereotypes-in-literature-are-affecting-artificial-intelligence\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Sexy women and brave men: Gender stereotypes in literature are affecting artificial intelligence\" \/>\n<meta property=\"og:description\" content=\"In literature, men are courageous, rational, and commit acts of violence. Women are typically sexy, pretty and fertile. The gender norms in the books influence how the machines of today learn human language.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/uniavisen.dk\/en\/sexy-women-and-brave-men-gender-stereotypes-in-literature-are-affecting-artificial-intelligence\/\" \/>\n<meta property=\"og:site_name\" content=\"University Post\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/uniavis\" \/>\n<meta property=\"article:published_time\" content=\"2019-09-13T04:52:21+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2019-09-13T09:51:15+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/uniavisen.dk\/wp-content\/uploads\/2019\/09\/tools.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"1219\" \/>\n\t<meta property=\"og:image:height\" content=\"860\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"Anne L\u00e6rke Rasmussen\" \/>\n<meta name=\"twitter:card\" 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army knife and eyelash curler","enable_alternative_caption":true,"alternative_caption":"Researchers have studied gendered language in 3.5 million books. Men are described with adjectives associated with action and behaviour. Women with words that are associated with appearance (and fertility)."},{"acf_fc_layout":"Standfirst","subject":"Research","text":"En forskergruppe har via maskinl\u00e6ring unders\u00f8gt k\u00f8nnet sprog i litteratur fra det 20. \u00e5rhundrede. Litteratur, der blandt andet har betydning for, hvordan nutidens programmer og maskiner, der genkender menneskesprog, opererer.","use_post_excerpt":true},{"acf_fc_layout":"Byline","is_author":true,"contributors":false},{"acf_fc_layout":"Content","content":"<p>In collaboration with an international research group, computer scientist at the University of Copenhagen <secret url=\"https:\/\/uniavisen.dk\/wp-content\/uploads\/2019\/09\/isabelle1-290x180.jpg\" text=\"Privat\/Isabelle Augenstein\">Isabelle Augenstein<\/secret> has studied language use in fiction and academic literature from 1900 to 2008. Using machine learning, researchers have identified the adjectives and verbs associated with nouns in 3.5 million books.<\/p>\n<blockquote><p>We do not just present the research results in itself, but also a general method on how to trace gender-biased language in larger collections of text<\/p>\n<p class=\"quotee\">Isabelle Augenstein, assistant professor at the Department of Computer Science<\/p>\n<\/blockquote>\n<p>The group’s research shows that there are significant differences in the literature on how female nouns (e.g. daughter, mother, princess, queen) and male nouns (father, son, brother, husband) are described with adjectives.<\/p>\n<p>The female gendered nouns are described with adjectives associated with their appearance, while men are described with words associated with their behaviour. While literature’s women are beautiful or sexy, men are primarily righteous and courageous.<\/p>\n<p>In sociolinguistics, researchers have for a long time been interested in the extent to which our language is gender specific. However, the computer scientists\u2019 latest research results have been achieved with new quantitative methods.<\/p>\n<p>\u00bbWe do not just present the research results by themselves, but also as a general method to trace gender-biased language in larger collections of text. A method that can be used on other data sets,\u00ab Isabella Augenstein writes in an email to the University Post.<\/p>\n<p>Via machine learning, the computer scientists have used a <secret text=\"The data set is based on the Google Ngram Corpus.\">huge data set<\/secret>, and they have been able to determine the extent of gendered language and whether the adjectives and verbs are positive or negative.<\/p>\n<h3>Affects today\u2019s society<\/h3>\n<p>Although the researchers have not used literature written after 2008, the gendered language of older texts is relevant for today: The algorithm used in programmes and machines that recognise people’s voices largely use texts available on the internet \u2013 including literary works and articles from many years ago. This goes for when smartphones recognise voices, or when Google prompts keywords.<\/p>\n<p>\u00bbA commonly used data set is, for example, the New York Times\u2019 data set, which contains articles from the period 1987-2007,\u00ab writes Isabelle Augenstein.<\/p>\n<p>The machines build up their ‘truth’ based on older texts that make use of gender stereotypes.<\/p>\n<div class=\"factbox\">\n<p class=\"factbox-header feature-color\">Read more<\/p>\n<p>The research article on the project, which was recently presented at the conference ACL 2019, can be found <a href=\"https:\/\/copenlu.github.io\/publication\/2019_acl_hoyle\/\">here.<\/a><\/p>\n<p> <\/p>\n<\/div>\n<p>\u00bbIn addition, I would argue that people still engage with older literature\u00ab. Older, written, language influences our society in the present: \u00bbThink of the classic children’s books like the Brothers Grimm\u2019s fairy tales or recent film adaptations of historical dramas,\u00ab writes Isabelle Augenstein.<\/p>\n<p>To follow up on the results, the research group which Isabelle Augenstein is a part of has just started a new study. It will be able to say more about how gender stereotypes have unfolded historically. They do this by taking the newly presented research results and dividing them into decades.<\/p>\n<p>\u00bbBased on what other researchers have found, we expect to be able to see that there are still gender stereotypes in modern books, even though they may be couched in a slightly different language,\u00ab she writes.<\/p>\n"},{"acf_fc_layout":"ArticleEnd"},{"acf_fc_layout":"Newsletter","lang_select":"Dansk","identifier":"Newsletter","headline":"Receive a weekly newsletter in your inbox","button_text":"Tilmeld nu","class":""},{"acf_fc_layout":"OtherStories","headline":"","hand_picked_posts":false,"references":false,"category":false,"theme":false,"number_of_posts":"4","style":"default"}]},"taxonomyData":{"category":[{"term_id":46,"name":"Science","slug":"science","term_group":0,"term_taxonomy_id":46,"taxonomy":"category","description":"","parent":0,"count":831,"filter":"raw"}],"post_tag":[{"term_id":741,"name":"Computer science","slug":"computer-science","term_group":0,"term_taxonomy_id":741,"taxonomy":"post_tag","description":"","parent":0,"count":5,"filter":"raw"},{"term_id":926,"name":"gender studies","slug":"gender-studies","term_group":0,"term_taxonomy_id":926,"taxonomy":"post_tag","description":"","parent":0,"count":9,"filter":"raw"},{"term_id":2307,"name":"isabelle augenstein","slug":"isabelle-augenstein-en","term_group":0,"term_taxonomy_id":2307,"taxonomy":"post_tag","description":"","parent":0,"count":1,"filter":"raw"},{"term_id":2308,"name":"kunstig intelligens","slug":"kunstig-intelligens-en","term_group":0,"term_taxonomy_id":2308,"taxonomy":"post_tag","description":"","parent":0,"count":2,"filter":"raw"},{"term_id":2277,"name":"k\u00f8n","slug":"koen-en","term_group":0,"term_taxonomy_id":2277,"taxonomy":"post_tag","description":"","parent":0,"count":2,"filter":"raw"},{"term_id":2311,"name":"maskinl\u00e6ring","slug":"maskinlaering-en","term_group":0,"term_taxonomy_id":2311,"taxonomy":"post_tag","description":"","parent":0,"count":1,"filter":"raw"},{"term_id":2312,"name":"navneord","slug":"navneord-en","term_group":0,"term_taxonomy_id":2312,"taxonomy":"post_tag","description":"","parent":0,"count":1,"filter":"raw"},{"term_id":2313,"name":"till\u00e6gsord","slug":"tillaegsord-en","term_group":0,"term_taxonomy_id":2313,"taxonomy":"post_tag","description":"","parent":0,"count":1,"filter":"raw"}],"post_format":[],"expression":[{"term_id":15,"name":"News Article","slug":"news_article","term_group":0,"term_taxonomy_id":15,"taxonomy":"expression","description":"","parent":0,"count":11489,"filter":"raw"}],"translation_priority":[]},"featured_media_url":"https:\/\/uniavisen.dk\/wp-content\/uploads\/2019\/09\/tools.jpg","_links":{"self":[{"href":"https:\/\/uniavisen.dk\/en\/wp-json\/wp\/v2\/posts\/93033","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/uniavisen.dk\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/uniavisen.dk\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/uniavisen.dk\/en\/wp-json\/wp\/v2\/users\/70"}],"replies":[{"embeddable":true,"href":"https:\/\/uniavisen.dk\/en\/wp-json\/wp\/v2\/comments?post=93033"}],"version-history":[{"count":5,"href":"https:\/\/uniavisen.dk\/en\/wp-json\/wp\/v2\/posts\/93033\/revisions"}],"predecessor-version":[{"id":93047,"href":"https:\/\/uniavisen.dk\/en\/wp-json\/wp\/v2\/posts\/93033\/revisions\/93047"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/uniavisen.dk\/en\/wp-json\/wp\/v2\/media\/92830"}],"wp:attachment":[{"href":"https:\/\/uniavisen.dk\/en\/wp-json\/wp\/v2\/media?parent=93033"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/uniavisen.dk\/en\/wp-json\/wp\/v2\/categories?post=93033"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/uniavisen.dk\/en\/wp-json\/wp\/v2\/tags?post=93033"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}