Algorithmic Bias Amplifies Queer Invisibility Online
Algorithmic biases can be defined as systematic and unintentional prejudices embedded in algorithms that shape how they function and interact with users. These biases have been identified as a significant issue in various aspects of digital life, particularly when it comes to social media platforms and their impact on user experiences. One such example is the way these biases may affect the representation of queer identities online. This article will explore the relationship between algorithmic bias and the invisibility or misrepresentation of queer individuals on social media platforms.
When people search for images or videos related to topics like gender identity or sexual orientation, Google's image recognition software often displays results that are either incorrect or stereotypical.
If you type "transgender" into an image search engine, you might see photos of men with short hair, or women who appear masculine. The same is true for searches related to gay and lesbian identity, where images depicting heteronormative couples or relationships dominate the results. This reflects the limited understanding of these concepts within the mainstream and suggests that those searching for information about them may encounter incomplete or even harmful representations.
Social media algorithms can contribute to the erasure of queer individuals by not promoting posts from LGBTQ+ individuals or organizations sufficiently. This means that the visibility of queer voices becomes marginalized, and the public discourse around queerness remains largely controlled by cis-heterosexual individuals or mainstream institutions. Queer people's content tends to get fewer likes, comments, shares, and engagements than non-queer content because of the lack of exposure they receive. This phenomenon has been observed across several social media platforms, including Twitter, Facebook, Instagram, and TikTok.
One possible explanation for this bias could be that social media algorithms prioritize popularity over diversity when determining which content to promote. These systems are designed to maximize engagement, so they favor posts that generate lots of reactions and clicks.
This algorithmic optimization process favors content that appeals to the majority rather than minorities like queer communities. This creates a vicious cycle in which the lack of representation leads to further marginalization, limiting queer users' ability to reach broader audiences and engage with their peers.
There is also evidence that suggests that algorithms may misrepresent queer identities intentionally.
Advertising algorithms have been known to target certain demographics with ads promoting conversion therapy or ex-gay movements. Such ads often use language such as "change your life" or "become the person you were meant to be," suggesting that one's gender identity or sexual orientation can be changed. Aside from the harmful effects on those who see these ads, it perpetuates the belief that there is something wrong with being LGBTQ+, contributing to the erasure of queer voices online.
Algorithmic biases contribute to the invisibility or misrepresentation of queer individuals online by limiting their visibility, prominence, and even amplifying damaging stereotypes. The prevalence of cis-heteronormative content dominating search results and platforms has consequences beyond just a lack of representation; it perpetuates harmful stereotypes and reinforces heterosexual normativity. It is essential for social media companies to recognize and address these biases and prioritize diverse voices to create more inclusive digital spaces.
Does algorithmic bias amplify queer invisibility or misrepresentation online?
While it is true that algorithmic bias may contribute to queer invisibility or misrepresentation online, there are several factors at play beyond this aspect alone. Firstly, queer individuals face discrimination both on the web and offline due to their sexual orientation or gender identity. This can lead to isolation from peers and family members as well as job loss.