Despite their ubiquity and importance in modern life, algorithmic feedback loops are often invisible to most people because they operate behind closed doors - literally! They have been embedded into everything from social media platforms like Facebook to dating apps like Tinder, and even online shopping websites.
What many don't realize is that these algorithms are also reinforcing very specific ideas about gendered sexual desire across cultures. This essay will explore how this happens and why it matters for everyone involved in today's digital age.
Algorithmic feedback loops refer to the systematic process by which machines learn from data inputs to produce more accurate outputs over time. In simple terms, they help computers understand what you want based on your past behavior so that they can provide better recommendations or suggestions in the future.
When you order something online, an algorithm may analyze your purchases and suggest similar products based on patterns it has noticed. When you scroll through Instagram, an algorithm might determine what posts to show you based on your likes and comments. And when you swipe left or right on Tinder, an algorithm might decide who you see next based on your previous choices. These systems learn from data inputs such as likes, clicks, swipes, and purchases to produce more personalized outcomes.
As we shall see below, these same mechanisms are being used to reinforce particular scripts of erotic desirability across different cultural contexts. By doing so, they narrow down our options for romantic partners while simultaneously promoting certain types of relationships over others. This can be harmful for both individuals seeking love and society at large.
Studies have shown that women are often portrayed as passive objects of male desire in dating apps like Tinder. The algorithms that drive these platforms prioritize photos featuring women who wear revealing clothing or make-up, posing provocatively or with their bodies displayed frontally. Men, meanwhile, are typically depicted as dominant figures whose sexuality is conveyed through physical prowess, confidence, or status markers like tattoos and cars. In other words, the algorithms are teaching us that there's only one way for men to be masculine and one way for women to be sexy - a very limiting message indeed!
What's more, this same dynamic can also play out in online shopping.
Amazon recently came under fire for recommending books about how to please one's husband alongside items related to housekeeping and parenting. This suggests that women should be seen primarily as domestic servants rather than as individuals with their own interests and needs outside the home. Such gendered scripts can create expectations around what it means to date or buy products from someone of a certain gender, ultimately leading to less diverse and less satisfying experiences all around.
Of course, these issues aren't limited to just one culture either. Studies have found similar patterns in China where young people use mobile dating apps such as Momo and Blued which encourage users to post shirtless selfies or engage in "flash mobs" where they dance sexually suggestively on live streams for money. These apps reinforce traditional ideas about gender roles by promoting heterosexual relationships between men who provide financial support and women who take care of the household.
Ultimately then, algorithmic feedback loops help perpetuate harmful stereotypes about sexual desire across different cultures, limiting our choices when it comes to romantic partners and online shopping options alike. By understanding how these mechanisms work, we can begin to challenge them and promote more inclusive alternatives that reflect the full range of human experience.
How do algorithmic feedback loops reinforce particular gendered scripts of erotic desirability across different cultural contexts?
According to some research, algorithmic feedback loops can play a significant role in shaping cultural norms around gendered perceptions of attractiveness. One study found that people who used dating apps with algorithms that matched them based on their profile pictures were more likely to have more traditional ideas about what makes someone physically attractive. This suggests that algorithmic feedback loops may reinforce certain types of beauty standards that privilege thinness, whiteness, and masculinity.