{"id":1789,"date":"2023-06-19T13:08:50","date_gmt":"2023-06-19T13:08:50","guid":{"rendered":"https:\/\/enactest-project.eu\/2023\/06\/19\/the-most-common-bias-in-data-is-gender-bias-heres-how-to-prevent-it\/"},"modified":"2023-06-19T13:08:50","modified_gmt":"2023-06-19T13:08:50","slug":"the-most-common-bias-in-data-is-gender-bias-heres-how-to-prevent-it","status":"publish","type":"post","link":"https:\/\/enactest-project.eu\/sv\/2023\/06\/19\/the-most-common-bias-in-data-is-gender-bias-heres-how-to-prevent-it\/","title":{"rendered":"The Most Common Bias in Data Is Gender Bias \u2014 Here\u2019s How to Prevent It"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\">Many hail data as \u201cthe great equalizer.\u201d After all, how can numbers be subjective? However, there is pushback to this. More and more people are becoming data-savvy and understand that data can be misrepresented. Which sounds more appealing: \u201cMy company size doubled\u201d or \u201cI hired another person\u201d? For someone just starting their business, the statements could be equally true, but they paint vastly different pictures.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full is-resized\"><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/enactest-project.eu\/wp-content\/uploads\/2023\/06\/graphs.jpg\" alt=\"An AI-generated image showing several ways of displaying graphic visualization. Image generated with AiTubo\" class=\"wp-image-1528\" width=\"512\" height=\"288\" title=\"\" srcset=\"https:\/\/enactest-project.eu\/wp-content\/uploads\/2023\/06\/graphs.jpg 1024w, https:\/\/enactest-project.eu\/wp-content\/uploads\/2023\/06\/graphs-300x169.jpg 300w, https:\/\/enactest-project.eu\/wp-content\/uploads\/2023\/06\/graphs-768x432.jpg 768w\" sizes=\"auto, (max-width: 512px) 100vw, 512px\" \/><\/figure>\n<\/div>\n\n\n<p class=\"wp-block-paragraph\">Sometimes, the error isn\u2019t in the representation, though; it\u2019s in the data. Small sample sizes or faulty polling can lead to bias in data. Complicating matters further is a lack of curiosity. People generally take data at face value, almost never investigating its source, its age, or the correlation between any of the variables.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">We obviously know that biased data is problematic, but many people fail to see how prevalent the issue actually is. In fact, there\u2019s a huge problem with data that consistently disenfranchises a huge portion of the population. This is known as gender bias. Typically, the percentage of women used in sample populations doesn\u2019t provide a true representation of consumers. At best, this is indicative of bad business intelligence. At worst, it actually endangers women.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Take automobiles, for instance. They\u2019re designed for men, and I don\u2019t mean aesthetically. The proportions, shape, ergonomics, and placement of interior features are designed to accommodate the male body. Worse yet, the safety research is based on the dimensions of men \u2014 leaving women more likely to be seriously injured in car accidents.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The same can be said for almost any product on the market. Smartphones are based on the size of a man\u2019s hand, which is typically larger. This might not sound like a big deal, but it has led to repetitive strain injury (RSI) in some women (and other populations, no doubt). When we don\u2019t work to keep our data free of gender bias, we\u2019re actively harming a large portion of the population.<\/p>\n\n\n<style>.wp-block-kadence-advancedheading.kt-adv-heading_c5a9f0-88, .wp-block-kadence-advancedheading.kt-adv-heading_c5a9f0-88[data-kb-block=\"kb-adv-heading_c5a9f0-88\"]{font-style:normal;}.wp-block-kadence-advancedheading.kt-adv-heading_c5a9f0-88 mark, .wp-block-kadence-advancedheading.kt-adv-heading_c5a9f0-88[data-kb-block=\"kb-adv-heading_c5a9f0-88\"] mark{font-style:normal;color:#f76a0c;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px;}<\/style>\n<h2 class=\"kt-adv-heading_c5a9f0-88 wp-block-kadence-advancedheading\" data-kb-block=\"kb-adv-heading_c5a9f0-88\">The Prevention of Gender Bias in Data<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Although it\u2019s not always an easy task, preventing gender bias in data is relatively straightforward \u2014 so there\u2019s no excuse not to try. In fact, it entails no more than two steps:<\/p>\n\n\n<style>.wp-block-kadence-advancedheading.kt-adv-heading_ecd4fe-c5, .wp-block-kadence-advancedheading.kt-adv-heading_ecd4fe-c5[data-kb-block=\"kb-adv-heading_ecd4fe-c5\"]{font-style:normal;}.wp-block-kadence-advancedheading.kt-adv-heading_ecd4fe-c5 mark, .wp-block-kadence-advancedheading.kt-adv-heading_ecd4fe-c5[data-kb-block=\"kb-adv-heading_ecd4fe-c5\"] mark{font-style:normal;color:#f76a0c;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px;}<\/style>\n<h3 class=\"kt-adv-heading_ecd4fe-c5 wp-block-kadence-advancedheading\" data-kb-block=\"kb-adv-heading_ecd4fe-c5\">1. Acknowledge that bias in data exists<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">The first step is admitting that data has a problem. Conscious and unconscious biases affect everyone, and our data is no exception. Look at the internet. Type \u201cperson cooking\u201d into a search engine, and the image results are mostly women. It\u2019s not that Google\u2019s algorithm is sexist \u2014 rather, it\u2019s pulling from what people upload. And because people are biased, those images primarily feature women.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Bias in data exists, and we need to take responsibility to correct the problem. Don\u2019t trust data blindly. Instead, research how the information was collected. If the data lacks equitable representation, question the research and consider purchasing products from a competitor.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">More importantly, build your teams with greater care, especially teams developing research models, collecting data, and deriving insights from the information. How diverse is your team? Do its members mirror the population to which the data will be applied? It all starts from the inside out. As with the data itself, you want a good mix of people \u2014 even more so, a mix of the demographics that will be using your product or service.<\/p>\n\n\n<style>.wp-block-kadence-advancedheading.kt-adv-heading_32b485-5e, .wp-block-kadence-advancedheading.kt-adv-heading_32b485-5e[data-kb-block=\"kb-adv-heading_32b485-5e\"]{font-style:normal;}.wp-block-kadence-advancedheading.kt-adv-heading_32b485-5e mark, .wp-block-kadence-advancedheading.kt-adv-heading_32b485-5e[data-kb-block=\"kb-adv-heading_32b485-5e\"] mark{font-style:normal;color:#f76a0c;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px;}<\/style>\n<h3 class=\"kt-adv-heading_32b485-5e wp-block-kadence-advancedheading\" data-kb-block=\"kb-adv-heading_32b485-5e\">2. Find the right sample population<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">The measurable characteristics of a group will skew without the right sample population. If you\u2019re developing a new product or service, it\u2019s important to first define your target audience. Who are they? Who is the intended user? And, most importantly, consider whether your own biases could mean you\u2019re overlooking potential audience segments.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Then, make sure you have a valid representation in your sample population. That\u2019s how to prevent gender bias \u2014 and other biases, for that matter \u2014 in data. From there, it\u2019s all about factoring in standard deviation, margin of error, and so on when developing and even testing your product or service.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Gender bias in data is real. The examples are many, and the solutions have been slow to come. It\u2019s up to everyone to work toward preventing gender bias \u2014 among all other biases \u2014 in the data that\u2019s captured, analysed, and utilized to make decisions involving products and services. It\u2019s as simple as that.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Many hail data as \u201cthe great equalizer.\u201d After all, how can numbers be subjective? However, there is&#8230;<\/p>\n","protected":false},"author":1,"featured_media":1556,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_kad_post_transparent":"","_kad_post_title":"","_kad_post_layout":"","_kad_post_sidebar_id":"","_kad_post_content_style":"","_kad_post_vertical_padding":"","_kad_post_feature":"","_kad_post_feature_position":"","_kad_post_header":false,"_kad_post_footer":false,"footnotes":""},"categories":[117],"tags":[503,504,505,506,238,507,121,122],"class_list":["post-1789","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-article-sv","tag-biased-data-sv","tag-ctg-sv","tag-data-representation-sv","tag-data-visualization-sv","tag-enactest-sv","tag-gender-bias-sv","tag-software-development-sv","tag-software-testing-sv"],"_links":{"self":[{"href":"https:\/\/enactest-project.eu\/sv\/wp-json\/wp\/v2\/posts\/1789","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/enactest-project.eu\/sv\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/enactest-project.eu\/sv\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/enactest-project.eu\/sv\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/enactest-project.eu\/sv\/wp-json\/wp\/v2\/comments?post=1789"}],"version-history":[{"count":0,"href":"https:\/\/enactest-project.eu\/sv\/wp-json\/wp\/v2\/posts\/1789\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/enactest-project.eu\/sv\/wp-json\/wp\/v2\/media\/1556"}],"wp:attachment":[{"href":"https:\/\/enactest-project.eu\/sv\/wp-json\/wp\/v2\/media?parent=1789"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/enactest-project.eu\/sv\/wp-json\/wp\/v2\/categories?post=1789"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/enactest-project.eu\/sv\/wp-json\/wp\/v2\/tags?post=1789"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}