{"id":68939,"date":"2023-07-11T00:06:38","date_gmt":"2023-07-11T04:06:38","guid":{"rendered":"https:\/\/world.edu\/?p=68939"},"modified":"2023-07-11T01:36:05","modified_gmt":"2023-07-11T05:36:05","slug":"ageism-sexism-classism-and-more-7-examples-of-bias-in-ai-generated-images","status":"publish","type":"post","link":"https:\/\/world.edu\/ageism-sexism-classism-and-more-7-examples-of-bias-in-ai-generated-images\/","title":{"rendered":"Ageism, sexism, classism and more: 7 examples of bias in AI-generated images"},"content":{"rendered":"<p><img fetchpriority=\"high\" decoding=\"async\" class=\"aligncenter size-full wp-image-68940\" src=\"https:\/\/world.edu\/wp-content\/uploads\/2023\/07\/ai_bias_board_1.0.jpg\" alt=\"\" width=\"680\" height=\"383\" srcset=\"https:\/\/world.edu\/wp-content\/uploads\/2023\/07\/ai_bias_board_1.0.jpg 680w, https:\/\/world.edu\/wp-content\/uploads\/2023\/07\/ai_bias_board_1.0-250x141.jpg 250w\" sizes=\"(max-width: 680px) 100vw, 680px\" \/><\/p>\n<p>If you\u2019ve been online much recently, chances are you\u2019ve seen some of the fantastical imagery created by text-to-image generators such as Midjourney and DALL-E 2. This includes everything from the\u00a0<a href=\"https:\/\/twitter.com\/snurb_dot_info\/status\/1637069897854484480?s=20\">naturalistic<\/a>\u00a0(think a soccer player\u2019s headshot) to the\u00a0<a href=\"https:\/\/twitter.com\/snurb_dot_info\/status\/1665336634055987200?s=20\">surreal<\/a>\u00a0(think a dog in space).<\/p>\n<p>&nbsp;<\/p>\n<blockquote class=\"twitter-tweet\">\n<p dir=\"ltr\" lang=\"en\">\u201cbeautiful pug astronaut floating in space chasing a bone chew toy and dog treats\u201d<\/p>\n<p>~ Prompt by Sil.Vicious <a href=\"https:\/\/twitter.com\/hashtag\/MidJourney?src=hash&amp;ref_src=twsrc%5Etfw\">#MidJourney<\/a> <a href=\"https:\/\/twitter.com\/hashtag\/AiArtwork?src=hash&amp;ref_src=twsrc%5Etfw\">#AiArtwork<\/a> <a href=\"https:\/\/t.co\/VEfiscny5Y\">pic.twitter.com\/VEfiscny5Y<\/a><\/p>\n<p>\u2014 Next Prompt (@next_prompt) <a href=\"https:\/\/twitter.com\/next_prompt\/status\/1647976990031454208?ref_src=twsrc%5Etfw\">April 17, 2023<\/a><\/p><\/blockquote>\n<p><script async src=\"https:\/\/platform.twitter.com\/widgets.js\" charset=\"utf-8\"><\/script><\/p>\n<p>Creating images using AI generators has never been simpler. At the same time, however, these outputs can reproduce biases and deepen inequalities, as our\u00a0<a href=\"https:\/\/urldefense.com\/v3\/__http:\/\/dx.doi.org\/10.1080\/21670811.2023.2229883__;!!NVzLfOphnbDXSw!E0CP_7rqJz5wJQ6MlscQdQjDeWVnmQHTahL2IztmxHSJkH7geXp-RIyjgQSTCJ-yOoUXmwW6Sya8YSHXIpmxq2ZOhKPbfsXBcnY%24\">latest research<\/a>\u00a0shows.<\/p>\n<h3><strong>How do AI image generators work?<\/strong><\/h3>\n<p>AI-based image generators use machine-learning models that take a text input and produce one or more images matching the description. Training these models requires massive datasets with millions of images.<\/p>\n<p>Although Midjourney is opaque about the exact way its algorithms work, most AI image generators use a process called diffusion. Diffusion models work by adding random \u201cnoise\u201d to training data, and then learning to recover the data by removing this noise. The model repeats this process until it has an image that matches the prompt.<\/p>\n<p>This is different to the large language models that underpin other AI tools such as ChatGPT. Large language models are trained on unlabelled text data, which they analyse to learn language patterns and produce human-like responses to prompts.<\/p>\n<h3><strong>How does bias happen?<\/strong><\/h3>\n<p>In generative AI, the input influences the output. If a user specifies they only want to include people of a certain skin tone or gender in their image, the model will take this into account.<\/p>\n<p>Beyond this, however, the model will also have a default tendency to return certain kinds of outputs. This is usually the result of how the underlying algorithm is designed, or a lack of diversity in the training data.<\/p>\n<p>Our study explored how Midjourney visualises seemingly generic terms in the context of specialised media professions (such as \u201cnews analyst\u201d, \u201cnews commentator\u201d and \u201cfact-checker\u201d) and non-specialised ones (such as \u201cjournalist\u201d, \u201creporter\u201d, \u201ccorrespondent\u201d and \u201cthe press\u201d).<\/p>\n<p>We started analysing the results in August last year. Six months later, to see if anything had changed over time, we generated additional sets of images for the same prompts.<\/p>\n<p>In total we analysed more than 100 AI-generated images over this period. The results were largely consistent over time. Here are seven biases that showed up in our results.<\/p>\n<h4><strong>1 and 2. Ageism and sexism<\/strong><\/h4>\n<p>For non-specialised job titles, Midjourney returned images of only younger men and women. For specialised roles, both younger and older people were shown \u2013 but the older people were always men.<\/p>\n<p>These results implicitly reinforce a number of biases, including the assumption that older people do not (or cannot) work in non-specialised roles, that only older men are suited for specialised work, and that less specialised work is a woman\u2019s domain.<\/p>\n<p>There were also notable differences in how men and women were presented. For example, women were younger and wrinkle-free, while men were \u201callowed\u201d to have wrinkles.<\/p>\n<p>The AI also appeared to present gender as a binary, rather than show examples of more fluid gender expression.<\/p>\n<div id=\"attachment_68941\" style=\"width: 690px\" class=\"wp-caption aligncenter\"><img decoding=\"async\" aria-describedby=\"caption-attachment-68941\" class=\"size-full wp-image-68941\" src=\"https:\/\/world.edu\/wp-content\/uploads\/2023\/07\/AIWomenaImage.jpg\" alt=\"\" width=\"680\" height=\"382\" srcset=\"https:\/\/world.edu\/wp-content\/uploads\/2023\/07\/AIWomenaImage.jpg 680w, https:\/\/world.edu\/wp-content\/uploads\/2023\/07\/AIWomenaImage-250x140.jpg 250w\" sizes=\"(max-width: 680px) 100vw, 680px\" \/><p id=\"caption-attachment-68941\" class=\"wp-caption-text\">AI showed women for inputs including non-specialised job titles such as journalist (right). It also only showed older men (but not older women) for specialised roles such as news analyst (left). Midjourney<\/p><\/div>\n<h4><strong>3. Racial bias<\/strong><\/h4>\n<p>All the images returned for terms such as \u201cjournalist\u201d, \u201creporter\u201d or \u201ccorrespondent\u201d exclusively featured light-skinned people. This trend of assuming whiteness by default is evidence of racial hegemony built into the system.<\/p>\n<p>This may reflect a lack of diversity and representation in the underlying training data \u2013 a factor that is in turn influenced by the general lack of\u00a0<a href=\"https:\/\/www.vox.com\/recode\/2020\/2\/18\/21121286\/algorithms-bias-discrimination-facial-recognition-transparency\">workplace diversity<\/a>\u00a0in the AI industry.<\/p>\n<div id=\"attachment_68942\" style=\"width: 690px\" class=\"wp-caption aligncenter\"><img decoding=\"async\" aria-describedby=\"caption-attachment-68942\" class=\"size-full wp-image-68942\" src=\"https:\/\/world.edu\/wp-content\/uploads\/2023\/07\/AIskincolour.jpg\" alt=\"\" width=\"680\" height=\"382\" srcset=\"https:\/\/world.edu\/wp-content\/uploads\/2023\/07\/AIskincolour.jpg 680w, https:\/\/world.edu\/wp-content\/uploads\/2023\/07\/AIskincolour-250x140.jpg 250w\" sizes=\"(max-width: 680px) 100vw, 680px\" \/><p id=\"caption-attachment-68942\" class=\"wp-caption-text\">The AI generated images with exclusively light-skinned people for all the job titles used in the prompts, including news commentator (left) and reporter (right). Midjourney<\/p><\/div>\n<h4><strong>4 and 5. Classism and conservatism<\/strong><\/h4>\n<p>All the figures in the images were also \u201cconservative\u201d in their appearance. For instance, none had tattoos, piercings, unconventional hairstyles, or any other attribute that could distinguish them from conservative mainstream depictions.<\/p>\n<p>Many also wore formal clothing such as buttoned shirts and neckties, which are markers of class expectation. Although this attire might be expected for certain roles, such as TV presenters, it\u2019s not necessarily a true reflection of how general reporters or journalists dress.<\/p>\n<h4><strong>6. Urbanism<\/strong><\/h4>\n<p>Without specifying any location or geographic context, the AI placed all the figures in urban environments with towering skyscrapers and other large city buildings. This is despite only slightly\u00a0<a href=\"https:\/\/www.worldbank.org\/en\/topic\/urbandevelopment\/overview\">more than half<\/a>\u00a0the world\u2019s population living in cities.<\/p>\n<p>This kind of bias has implications for how we see ourselves, and our degree of connection with other parts of society.<\/p>\n<div id=\"attachment_68943\" style=\"width: 690px\" class=\"wp-caption aligncenter\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-68943\" class=\"size-full wp-image-68943\" src=\"https:\/\/world.edu\/wp-content\/uploads\/2023\/07\/AIlocation.jpg\" alt=\"\" width=\"680\" height=\"382\" srcset=\"https:\/\/world.edu\/wp-content\/uploads\/2023\/07\/AIlocation.jpg 680w, https:\/\/world.edu\/wp-content\/uploads\/2023\/07\/AIlocation-250x140.jpg 250w\" sizes=\"(max-width: 680px) 100vw, 680px\" \/><p id=\"caption-attachment-68943\" class=\"wp-caption-text\">Without specifying a geographic context, and with a location-neutral job title, AI assumed an urban context for the images, including reporter (left) and correspondent (right). Midjourney<\/p><\/div>\n<h4><strong>7. Anachronism<\/strong><\/h4>\n<p>Digital technology was underrepresented in the sample. Instead, technologies from a distinctly different era \u2013 including typewriters, printing presses and oversized vintage cameras \u2013 filled the samples.<\/p>\n<p>Since many professionals look similar these days, the AI seemed to be drawing on more distinct technologies (including historical ones) to make its representations of the roles more explicit.<\/p>\n<div id=\"attachment_68945\" style=\"width: 690px\" class=\"wp-caption aligncenter\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-68945\" class=\"size-full wp-image-68945\" src=\"https:\/\/world.edu\/wp-content\/uploads\/2023\/07\/AIanachronisim.jpg\" alt=\"\" width=\"680\" height=\"382\" srcset=\"https:\/\/world.edu\/wp-content\/uploads\/2023\/07\/AIanachronisim.jpg 680w, https:\/\/world.edu\/wp-content\/uploads\/2023\/07\/AIanachronisim-250x140.jpg 250w\" sizes=\"(max-width: 680px) 100vw, 680px\" \/><p id=\"caption-attachment-68945\" class=\"wp-caption-text\">AI used anachronistic technology, including vintage cameras, typewriters and printing presses, when depicting certain occupations such as the press (left) and journalist (right). Images by the authors via Midjourney<\/p><\/div>\n<p>The next time you see AI-generated imagery, ask yourself how representative it is of the broader population and who stands to benefit from the representations within.<\/p>\n<p>Likewise, if you\u2019re generating images yourself, consider potential biases when crafting your prompts. Otherwise you might unintentionally reinforce the same harmful stereotypes society has spent decades trying to unlearn.<\/p>\n<p><em><strong>Author Bios:<\/strong> T.J. Thomson is a Senior Lecturer in Visual Communication &amp; Digital Media at RMIT University and Ryan J. Thomas is Assistant Professor, Journalism Studies at the University of Missouri-Columbia<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"<p>If you\u2019ve been online much recently, chances are you\u2019ve seen some of the fantastical imagery created by text-to-image generators such as Midjourney and DALL-E 2. This includes everything from the\u00a0naturalistic\u00a0(think a soccer player\u2019s headshot) to the\u00a0surreal\u00a0(think a dog in space). &nbsp; \u201cbeautiful pug astronaut floating in space chasing a bone chew toy and dog treats\u201d [&hellip;]\n&hellip; <a href=\"https:\/\/world.edu\/ageism-sexism-classism-and-more-7-examples-of-bias-in-ai-generated-images\/\" class=\"more\">learn more&rarr;<\/a><\/p>","protected":false},"author":33,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[59],"tags":[434524],"coauthors":[11733],"class_list":["post-68939","post","type-post","status-publish","format-standard","hentry","category-science-tech","tag-ai-generated-images"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.2 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Ageism, sexism, classism and more: 7 examples of bias in AI-generated images - World leading higher education information and services<\/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:\/\/world.edu\/ageism-sexism-classism-and-more-7-examples-of-bias-in-ai-generated-images\/\" class=\"yoast-seo-meta-tag\" \/>\n<meta property=\"og:locale\" content=\"en_US\" class=\"yoast-seo-meta-tag\" \/>\n<meta property=\"og:type\" content=\"article\" class=\"yoast-seo-meta-tag\" \/>\n<meta property=\"og:title\" content=\"Ageism, sexism, classism and more: 7 examples of bias in AI-generated images - World leading higher education information and services\" class=\"yoast-seo-meta-tag\" \/>\n<meta property=\"og:description\" content=\"If you\u2019ve been online much recently, chances are you\u2019ve seen some of the fantastical imagery created by text-to-image generators such as Midjourney and DALL-E 2. This includes everything from the\u00a0naturalistic\u00a0(think a soccer player\u2019s headshot) to the\u00a0surreal\u00a0(think a dog in space). &nbsp; \u201cbeautiful pug astronaut floating in space chasing a bone chew toy and dog treats\u201d [&hellip;]\" class=\"yoast-seo-meta-tag\" \/>\n<meta property=\"og:url\" content=\"https:\/\/world.edu\/ageism-sexism-classism-and-more-7-examples-of-bias-in-ai-generated-images\/\" class=\"yoast-seo-meta-tag\" \/>\n<meta property=\"og:site_name\" content=\"World leading higher education information and services\" class=\"yoast-seo-meta-tag\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-11T04:06:38+00:00\" class=\"yoast-seo-meta-tag\" \/>\n<meta property=\"article:modified_time\" content=\"2023-07-11T05:36:05+00:00\" class=\"yoast-seo-meta-tag\" \/>\n<meta property=\"og:image\" content=\"https:\/\/world.edu\/wp-content\/uploads\/2023\/07\/ai_bias_board_1.0.jpg\" class=\"yoast-seo-meta-tag\" \/>\n<meta name=\"author\" content=\"Kevin\" class=\"yoast-seo-meta-tag\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" class=\"yoast-seo-meta-tag\" \/>\n\t<meta name=\"twitter:data1\" content=\"Kevin\" class=\"yoast-seo-meta-tag\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" class=\"yoast-seo-meta-tag\" \/>\n\t<meta name=\"twitter:data2\" content=\"6 minutes\" class=\"yoast-seo-meta-tag\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\\\/\\\/world.edu\\\/ageism-sexism-classism-and-more-7-examples-of-bias-in-ai-generated-images\\\/#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/world.edu\\\/ageism-sexism-classism-and-more-7-examples-of-bias-in-ai-generated-images\\\/\"},\"author\":{\"name\":\"Kevin\",\"@id\":\"https:\\\/\\\/world.edu\\\/#\\\/schema\\\/person\\\/42c1c1d5ff7ffbdf0bd50ed94c6ae6ee\"},\"headline\":\"Ageism, sexism, classism and more: 7 examples of bias in AI-generated images\",\"datePublished\":\"2023-07-11T04:06:38+00:00\",\"dateModified\":\"2023-07-11T05:36:05+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/world.edu\\\/ageism-sexism-classism-and-more-7-examples-of-bias-in-ai-generated-images\\\/\"},\"wordCount\":1024,\"image\":{\"@id\":\"https:\\\/\\\/world.edu\\\/ageism-sexism-classism-and-more-7-examples-of-bias-in-ai-generated-images\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/world.edu\\\/wp-content\\\/uploads\\\/2023\\\/07\\\/ai_bias_board_1.0.jpg\",\"keywords\":[\"AI-generated images\"],\"articleSection\":[\"Science and Technology\"],\"inLanguage\":\"en-US\"},{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/world.edu\\\/ageism-sexism-classism-and-more-7-examples-of-bias-in-ai-generated-images\\\/\",\"url\":\"https:\\\/\\\/world.edu\\\/ageism-sexism-classism-and-more-7-examples-of-bias-in-ai-generated-images\\\/\",\"name\":\"Ageism, sexism, classism and more: 7 examples of bias in AI-generated images - 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