Algorithmic recommendation systems play an increasingly important role in shaping what we see online. These systems are designed to analyze user behavior and preferences to suggest relevant content that is likely to interest them.
They often perpetuate heteronormative assumptions about gender and sexuality, which can be problematic when it comes to recommending erotic content.
If a user views mainstream pornography featuring cisgender men and women having vanilla sex, algorithmic recommendation systems may assume that this individual prefers traditional forms of erotica and recommend more of the same. This can limit the diversity of options available to users who identify as LGBTQ+ or have non-traditional sexual desires. In addition, these systems may reinforce harmful stereotypes about what constitutes "normal" sexuality, excluding those who do not conform to societal norms from accessing suitable materials.
Algorithmic recommendation systems also rely on metadata and keywords to categorize content, which can result in inaccurate or incomplete descriptions of the material's sexual nature.
If a video includes a few seconds of explicit footage but is primarily focused on storytelling or character development, it might be labeled as "general interest," making it less likely to appear in recommendations for viewers seeking out explicit material. This can result in a homogenization of content and a lack of variety in terms of representation.
To address these issues, there needs to be greater transparency and accountability within algorithmic recommendation systems. Companies should hire diverse teams to develop their algorithms, ensuring that all perspectives are considered during the design process. They should also provide clear and accurate metadata describing the sexual nature of each piece of content, allowing viewers to make informed decisions based on their preferences rather than relying solely on automated suggestions.
Platforms should prioritize inclusion by showcasing a wide range of content that caters to different tastes and demographics. By doing so, they can create a more welcoming environment for all users, regardless of their gender identity or sexual orientation.
How do algorithmic recommendation systems reinforce heteronormativity in the consumption of erotic content online?
Algorithmic recommendation systems have been shown to reinforce heteronormative patterns of behavior by promoting a narrow range of sexual orientations and preferences. This is due to several factors that include the dominance of cisgender and straight individuals within the development teams behind these systems, as well as the algorithms' reliance on data gathered from mainstream platforms like YouTube and Pornhub.