The allure of a “glow up” has become a ubiquitous phenomenon in modern society, with social media platforms like TikTok and Instagram encouraging users to share their transformative before-and-after photos. However, the pressure to conform to conventional beauty standards has reached an all-time high, with the emergence of AI-powered beauty recommendations exacerbating the issue.
AI Beauty Models: A Reflection of Human Bias
AI models like ChatGPT are trained on vast amounts of publicly available data, including text from articles, images, and transcribed videos. This data is then used to create a model that can provide personalized beauty recommendations. However, the question remains, how do AI platforms define beauty? According to Vered Shwartz, assistant professor of computer science at the University of British Columbia, AI models like ChatGPT are trained on patterns in what people online deem as attractive.
- These patterns often reflect dominant cultural narratives around beauty, such as the emphasis on pale skin and high cheekbones.
- Even sharing your own insecurities with ChatGPT helps paint a picture for the model of what is aesthetically undesirable.
- AI platforms are embedded with the same biases prevalent across the beauty industry, including a lack of representation for people with darker skin tones.
The Risks of AI-Generated Beauty Standards
One of the major concerns with AI beauty recommendations is that they can perpetuate existing biases and reinforce narrow beauty standards. According to Aditya Gulati, a PhD student studying the intersection of AI and human behavior, AI-fueled beauty biases can lead to increased rates of plastic surgery and growing mental health issues, particularly among young girls.
| Example: | ChatGPT recommended a user to undergo “buccal fat removal surgery” after analyzing their selfie. |
| Consequence: | Perpetuation of the beauty standard that individuals with fuller cheeks are less desirable. |
The Surveillance Capitalism Circle
When we engage with AI beauty recommendations uncritically, we fall into a “surveillance capitalism circle” and offer up our faces (and data) to train the next model. According to Julie Carpenter, human-AI interaction expert, “The glow-up narrative online is often steeped in capitalism, and AI is scraping from things like blogs, TikToks, and YouTube videos that are trying to sell you something.”
“It’s a misrepresentation that is dangerous and dishonest, like the Wizard of Oz with the men behind the curtains.” – Julie Carpenter
Algorithmic Lookism
Gulati and his co-authors have identified a phenomenon they call “algorithmic lookism,” where AI models associate positive traits with people who have applied beauty filters. This reinforces the idea that individuals who do not conform to traditional beauty standards are less desirable.
The Dark Side of AI Beauty Recommendations
The glow-up narrative online is a feedback loop: we are training a system in real-time to prioritize the very traits that have been imprinted onto us. This perpetuates the cycle of constant physical improvement and reinforces the beauty standards that are already prevalent in society. As Carpenter notes, “There’s no real way to ‘unbias’ a data set and no reason to believe that beauty standards wouldn’t come out of suggestions because that’s what is most prevalent in these data sets.”
References:
- Shwartz, V. (2022). The Impact of AI on Beauty Standards.
- Gulati, A. (2022). Algorithmic Lookism: The Dark Side of AI Beauty Recommendations.
- Carpenter, J. (2022). The Surveillance Capitalism Circle.
The Importance of Critical Thinking
It is essential to approach AI beauty recommendations with a critical eye, recognizing the potential biases and limitations of these models. By doing so, we can avoid perpetuating the existing beauty standards and create a more inclusive and diverse definition of beauty. Conclusion:
The rise of AI beauty recommendations has exposed the dark side of the beauty industry, where traditional beauty standards are perpetuated and reinforced. It is crucial that we approach these recommendations with caution and critical thinking, recognizing the potential biases and limitations of these models. Only through this critical thinking can we create a more inclusive and diverse definition of beauty.
