And \u2018to perform more poorly\u2019 may mean fatally so. Looking behind statistics that might seem hard to explain, like women\u2019s higher rate of injury and death in car accidents<\/a><\/u>, Criado-Perez found cars are safety tested with crash test dummies designed around an average male\u2019s weight and measurements. Similarly, the UK\u2019s Trade Union Council (TUC) lists multiple examples of workplace safety equipment not designed to fit women<\/a><\/u>, alongside cases of serious injuries and fatalities directly linked to poorly fitting equipment. It\u2019s not uncommon to find medicines trialed only on men, even when the drug is only for women<\/a><\/u>. Everywhere you look, according to Criado-Perez\u2019s research. But since we\u2019re in cybersecurity, let\u2019s draw some examples from tech, software and the internet.<\/p>\n Researcher at MIT Media Lab Joy Buolamwini found facial recognition software was more likely to misidentify women, and especially <\/a>B<\/a>lack women<\/a><\/u>.<\/p>\n While it correctly identified white men 99 percent of the time, the results fell to 93 when identifying white women, and plummeted to just 35 percent for Black women.<\/p>\n<\/blockquote>\n Voice recognition systems also fail more frequently for women\u2019s voices. University of Washington\u2019s Dr. Rachael Tatman found YouTube\u2019s auto-captioning accurately identified men\u2019s speech 13 percent more often than women\u2019s<\/a><\/u>.<\/p>\n When Head of Social Insight at OgilvyOne in 2015, Karin Robinson analyzed her own Twitter following. She found Twitter\u2019s algorithm that supposedly identifies users\u2019 age, gender and income (for marketers\u2019 use in targeting ads) was out by a significant margin when it came to her followers<\/a><\/u> \u2013 under-counting women and over-counting men.<\/p>\n While the consequences of misidentifying users\u2019 gender for marketing purposes might seem trivial, it may also mean analyses Twitter performs on its own data, like checking whether they\u2019re handling online abuse or bullying complaints in a gender-neutral way, will be compromised.<\/p>\n Amazon realized in 2015 that the AI it created to shortlist candidates\u2019 resumes had developed bias<\/a><\/u> similar to that of humans when shortlisting candidates for roles.<\/p>\n The technology had learned its ways from patterns of employment over the past 10 years.<\/p>\n Because most people who apply and are appointed for software developer roles are men, the AI decided a resume containing words like \u2018women\u2019s\u2019 (for example, \u2018women\u2019s college\u2019) should go to the bottom of the pile.<\/p>\n<\/blockquote>\n Amazon no longer uses the AI for shortlisting, in part because of this finding.<\/p>\n Even if you think you don\u2019t perform or use research, you probably do, somewhere along the line. From user testing software, to customer satisfaction surveys, if you\u2019re not looking to understand how well you\u2019re doing and how to improve, you should be.<\/p>\n There are several things you can do to reduce gender bias in the data you rely upon to do your work.<\/p>\n Many businesses are now starting to realize that determining your potential audience by your current audience limits growth. If you make design software, for example, and know it\u2019s currently most popular with men ages 35 to 45, rather than aiming to increase your market by reaching more of the same people, there may be bigger gains in catering to groups you\u2019re not reaching.<\/p>\n Many brands once thought altering their product or marketing to reach more women would always alienate an existing male market, but prominent examples show the male market is not so easily threatened. For example, when a number of movie studios started releasing action films centered around female characters, like Mad Max: Fury Road<\/a><\/u> and Star Wars: The Force Awakens<\/a><\/u>, their box office success now eclipses the concerns that preceded their release \u2013 that the popular franchises were taking a huge risk with an existing male fan base.<\/p>\n One reason voice recognition software fails more often with female voices may be that popular speech and language open data sources, such as TED talks<\/a><\/u>, are male-dominated.<\/p>\n Asking your researchers, or companies you commission research from, where their data comes from, and whether broader sources are available, will make results less likely to favor groups already over-represented.<\/p>\n People who love what you do are always easiest to research, but they\u2019ll teach you the least. You shouldn\u2019t be trying to improve your product or expand your market by always asking fans, or existing customers, what they think.<\/p>\n It\u2019s much harder to connect with \u2018the one that got away\u2019 (or even, the one who\u2019s never heard of you) but they\u2019re who you most need to talk to.<\/p>\n Caroline Criado-Perez notes in her book that one reason for the gender data gap is women step up to take part in research less often than men and more often drop out of studies.<\/p>\n What reasons lie behind this? It\u2019s hard to say, but we know women are more likely to face financial pressure<\/a><\/u> and have caring responsibilities<\/a><\/u>, so they\u2019ll naturally struggle more to give up their time. Considering how you design research, and whether it\u2019s equally appealing to men and women, could help. Here are some research design factors to consider.<\/p>\n Asking staff to share an invitation on social media will likely reach people like your staff.<\/p>\n If requested to do so, a good research company will be able to find you participants matching a balance of profiles, such as age, profession and gender.<\/p>\n Inviting women specifically in the advertising description can also help.<\/p>\n A lunchtime focus group might be easier for women with caring responsibilities, rather than one held after work.<\/p>\n A 20-minute interview will attract more participants than a one-hour interview.<\/p>\n Review and reduce any potential personal safety risks for those who take part. For example, how are you making sure participants never see each other\u2019s personal data? If you\u2019re asking people to go somewhere to take part, do the lighting, parking or connections to public transport feel safe?<\/p>\n Women may feel more comfortable meeting a researcher in a public place, like a coffee shop or library, rather than going to an unknown office building.<\/p>\n Even with studies that are well paid, more people will be able to take part if they don\u2019t have to travel. Consider whether the researcher can go to the participant or meet over video conferencing rather than face-to-face.<\/p>\n In Invisible Women, Criado-Perez says sometimes the gender data gap problem isn\u2019t that the data on women is missing, but that it\u2019s mixed in with men\u2019s data or not analyzed separately to see if there are differences. This hides when something isn\u2019t working for one gender or another. For example, if your male users are having an above-average experience and your female users are having a below-average experience, without delineating the data according to which subjects are men, and which are women, the results will tell you it\u2019s fine for everyone.<\/p>\n By gathering gender information about participants and making sure any analysis looks for potential differences between men and women, you can identify gender-related problems early.<\/p>\n A product can only be as good as the research used to develop it. It\u2019s good business sense to make sure the data that informs you includes your whole intended audience, not just those who are easiest to engage.<\/p>\n<\/blockquote>\n The notion of the gender data gap has given consumers a language to identify and object to the gender data biases behind products, from software to scissors. Business leaders can build a better reputation, and better products, by asking the right questions. Make sure the research you rely on is representative and allows for the possibility that the gender of the person using your product might change how good it is for them.<\/p>\n","protected":false},"excerpt":{"rendered":" We all rely on research in some way, to do our jobs. But sometimes that research hasn\u2019t included women, or has assumed women and men are the same.<\/p>\n","protected":false},"author":2552,"featured_media":33823,"template":"","coauthors":[3673],"class_list":{"0":"post-33822","1":"emagazine","2":"type-emagazine","3":"status-publish","4":"has-post-thumbnail","6":"emagazine-category-data-and-privacy","7":"emagazine-category-opinions","8":"emagazine-category-women-and-diversity","9":"emagazine-tag-data","10":"emagazine-tag-professional-advice","11":"emagazine-tag-women"},"hreflang":[{"hreflang":"x-default","url":"https:\/\/www.kaspersky.com\/blog\/secure-futures-magazine\/why-lack-data-about-women\/33822\/"},{"hreflang":"en-us","url":"https:\/\/usa.kaspersky.com\/blog\/secure-futures-magazine\/why-lack-data-about-women\/21744\/"}],"acf":[],"_links":{"self":[{"href":"https:\/\/www.kaspersky.com\/blog\/wp-json\/wp\/v2\/emagazine\/33822","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.kaspersky.com\/blog\/wp-json\/wp\/v2\/emagazine"}],"about":[{"href":"https:\/\/www.kaspersky.com\/blog\/wp-json\/wp\/v2\/types\/emagazine"}],"author":[{"embeddable":true,"href":"https:\/\/www.kaspersky.com\/blog\/wp-json\/wp\/v2\/users\/2552"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.kaspersky.com\/blog\/wp-json\/wp\/v2\/media\/33823"}],"wp:attachment":[{"href":"https:\/\/www.kaspersky.com\/blog\/wp-json\/wp\/v2\/media?parent=33822"}],"wp:term":[{"taxonomy":"author","embeddable":true,"href":"https:\/\/www.kaspersky.com\/blog\/wp-json\/wp\/v2\/coauthors?post=33822"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
\n![\"lack](\"https:\/\/media.kasperskydaily.com\/wp-content\/uploads\/sites\/92\/2020\/02\/16104608\/200305-kaspersky-m131-why-lack-data-about-women-inline-1024x768.jpg\")
\nThe significance of this topic is such that Criado-Perez\u2019s book<\/a><\/u> Invisible Women: Exposing Data Bias in a World Designed For Men won the Royal Society\u2019s Science Book Prize in 2019<\/a><\/u> and Financial Times Business Book of the Year<\/a><\/u>. Several conferences to be held in 2020 (for example, Gender Data Gap Conference in Stockholm<\/a><\/u> and Measure what Matters in Canberra<\/a><\/u>) aim to educate participants to think about, and act on, the gender data gap.<\/p>\nWhere are the gender data gaps?<\/h2>\n
Software doesn\u2019t identify women\u2019s voices or faces so well<\/h2>\n
Twitter reports many women are in fact, men<\/h2>\n
AI taught itself to discriminate against women<\/h2>\n
What can business leaders in tech do?<\/h2>\n
1.\u00a0\u00a0 Consider women a potential market you\u2019ve not yet reached<\/h3>\n
2.\u00a0\u00a0 Check where your data comes from<\/h3>\n
3.\u00a0\u00a0 Research your cold audiences<\/h3>\n
4. To recruit more women, change your strategy<\/h3>\n
Recruitment methods<\/h4>\n
Timing<\/h4>\n
Time commitment<\/h4>\n
Safety<\/h4>\n
Travel<\/h4>\n
5. Delineate data for men and women, and analyze it separately<\/h3>\n