Social media has exacerbated the promotion of Western beauty norms, leading to negative self-image, particularly in women and girls, and causing harm such as body dysmorphia. Increasingly content on the internet has been artificially generated, leading to concerns that these norms are being exaggerated. The aim of this work is to study how generative AI models may encode 'beauty' and erase 'ugliness', and discuss the implications of this for society. To investigate these aims, we create two image generation pipelines: a text-to-image model and a text-to-language model-to image model. We develop a structured beauty taxonomy which we use to prompt three language models (LMs) and two text-to-image models to cumulatively generate 5984 images using our two pipelines. We then recruit women and non-binary social media users to evaluate 1200 of the images through a Likert-scale within-subjects study. Participants show high agreement in their ratings. Our results show that 86.5% of generated images depicted people with lighter skin tones, 22% contained explicit content despite Safe for Work (SFW) training, and 74% were rated as being in a younger age demographic. In particular, the images of non-binary individuals were rated as both younger and more hypersexualised, indicating troubling intersectional effects. Notably, prompts encoded with 'negative' or 'ugly' beauty traits (such as "a wide nose") consistently produced higher Not SFW (NSFW) ratings regardless of gender. This work sheds light on the pervasive demographic biases related to beauty standards present in generative AI models -- biases that are actively perpetuated by model developers, such as via negative prompting. We conclude by discussing the implications of this on society, which include pollution of the data streams and active erasure of features that do not fall inside the stereotype of what is considered beautiful by developers.

An ad campaign by Dove in 2024 was designed to highlight the biases in AI. In the advert, an image generator is asked to create the most beautiful woman in the world and produces virtually indistinguishable women who are young, thin and white, with blonde hair and blue eyes. The images generated look similar to the Guess AI model. Hayward worries that seeing these unattainable images could have an impact on people's mental health and negatively affect their body image. Concern around unrealistic beauty standards and the damaging effects they can have is nothing new.

The beauty pageant industry isn't what it used to be. Miss Universe, which has been around since 1952, has suffered a dramatic ratings decline in the last five years. In May, the reigning Miss USA and Miss Teen USA gave back their crowns, sparking fresh controversy in the community. Yet, a new kind of beauty pageant has emerged. This pageant is similar in many ways to the traditional experience, except for one important detail: the women are not real. The World AI Creators Awards (WAICAS) has gathered 10 finalists in their quest to find "Miss AI," the winner of a beauty pageant for women generated by artificial intelligence (AI).

She doesn't actually exist, but, if things go my way, she's going to be the world's first "Miss AI". I recently created her image on a website that generates AI faces and then entered her into a beauty pageant. Now I am sitting back in anticipation of netting the 20,000 grand prize. What fresh hell is this, you ask? Well, I regret to inform you that AI beauty pageants are a thing now.

Unsupported and unfalsifiable claims we encounter in our daily lives can influence our view of the world. Characterizing, summarizing, and -- more generally -- making sense of such claims, however, can be challenging. In this work, we focus on fine-grained debate topics and formulate a new task of distilling, from such claims, a countable set of narratives. We present a crowdsourced dataset of 12 controversial topics, comprising more than 120k arguments, claims, and comments from heterogeneous sources, each annotated with a narrative label. We further investigate how large language models (LLMs) can be used to synthesise claims using In-Context Learning. We find that generated claims with supported evidence can be used to improve the performance of narrative classification models and, additionally, that the same model can infer the stance and aspect using a few training examples. Such a model can be useful in applications which rely on narratives , e.g. fact-checking.

Every winter, households across Britain get misty-eyed at the latest Christmas advert from John Lewis. From 2021's'Unexpected Guest' featuring an alien crash-landing on Earth, to 2016's'Buster the Boxer' starring a dog leaping on a trampoline, these adverts pull on our heartstrings like no other. While there's still four months to go until Christmas rolls around, some people are already so excited for the advert that they're speculating what it could be about. Its script features a pocket watch left in a cozy coffee shop in a picturesque snowy village - but does it live up to John Lewis' reputation? The ad opens on a snowy village with a cozy coffee shop at the heart.

Researchers have used artificial intelligence to create'ideal' pageant queen candidates as part of a study to explore the beauty standards of Miss United Kingdom and other global contests. The experts at Great Green Wall used online image generator Midjourney to do this, which gave a surprising variety of results for each country. While Miss United Kingdom was thought to have been influenced by Princess Diana, other nations were inspired by athletes, Bollywood and even Marilyn Monroe. Yet these images often included'highly unobtainable body proportions', researchers said, with'supermodel-like facial structures that can only be achieved through cosmetic surgery or genetics'. Founder of Great Green Wall, Sam Phoenix, wrote: 'Beauty standards can vary drastically from country to country, so it was fascinating to see how well the AI was able to recreate those unique beauty standards within a "pageant" setting.

Joy Buolamwini is not only intelligent, she is effective and absolutely stunning. From the article: "Olay is launching a new campaign to help end discriminatory computer algorithms that skew standards of beauty, per an announcement emailed to Marketing Dive. The effort coincides with National Coding Week (Sept. The Procter & Gamble-owned brand is also teaming with [computer scientist and] activist Joy Buolamwini, founder of the Algorithmic Justice League, to conduct an audit of its own practices."

Ideas about what constitutes "beauty" are complex, subjective, and by no means limited to physical appearances. Elusive though it is, everyone wants more of it. That means big business and increasingly, people harnessing algorithms to create their ideal selves in the digital and, sometimes, physical worlds. In this episode, we explore the popularity of beauty filters, and sit down with someone who's convinced his software will show you just how to nip and tuck your way to a better life. This episode was reported by Tate Ryan-Mosley, and produced by Jennifer Strong, Emma Cillekens, Karen Hao and Anthony Green. Strong: Beauty has always been one of society's greatest obsessions. And for as long as we've worshipped it… we've also found ways to change and enhance it. From makeup and clothes... to airbrushing photos… or a surgical nip and tuck. Strong: You may not realize it...but this technology is right at your fingertips.