Algorithms are at the center of the online experience, identifying which content, articles and advertisements will appear in people’s feeds. Initially designed to tailor content for users and boost engagement, these systems now raise a critical question — do they simply reflect people’s interests or are they actively shaping them? Evidence suggests it might be the latter.
Algorithms were engineered to analyze large volumes of data and predict what content would be most likely to capture the user’s attention. Platforms like Instagram and Facebook that collect data use these predictions to recommend videos, images and articles that align with users’ past interactions.
In this way, systems are intended to increase user satisfaction by delivering personalized content. When a user’s feed is filled with familiar content, it not only increases engagement but also helps platforms generate revenue by leveraging targeted ads.
Yet beneath the surface lies a darker reality to this tailored experience. The same algorithms meant to serve users’ interests can limit their exposure to diverse viewpoints. By steadily curating content that affirms users’ existing beliefs, systems create so-called “filter bubbles” where opposing views are rarely represented.
For example, when it comes to more complex issues regarding politics or societal issues, the facts presented are often those that algorithms predict will keep users engaged, rather than providing a balanced view of the situation.
This trend toward selective exposure has broader implications. A study by the Pew Research Center found that nearly 71% of individuals aged 16 to 40 consume news through social media on a daily basis, meaning that most of the respondents are typically exposed to narrow, curated content.
This reliance on algorithmically determined content makes users more vulnerable to consuming misinformation. This personalized content curation can lead to people lacking a well-rounded understanding of critical issues.
Furthermore, the impact of personalization goes beyond social media. In the scope of gender inequality, research by Anja Lambrecht and Catherine Tucker found that STEM job ads were disproportionately targeting men.
This could be an indication that algorithms categorize users based on observed probabilities rather than recognizing them as unique individuals. Machine characteristics are simplifying identities into data points, which can lead to biases affecting societal perceptions.
“Machines aren’t just reflecting our biases,” Caroline Criado Perez noted in “Invisible Women: Data Bias in a World Designed for Men.” “Sometimes they are amplifying them – and by a significant amount.”
It’s critical to note that behind every machine, human choices are at play. The decision about what data to collect, which variables to prioritize and how algorithms can personalize feeds based on predictions ultimately shape the digital landscape.
While these systems can predict interests, the collected data can also shape those interests by continually reinforcing existing beliefs. In the “Cambridge Analytica Scandal” of 2018, users felt vulnerable because of harvested data profiles from Facebook that were sold to presidential campaigns.
While algorithms can enhance the human digital experience through personalized content curation to boost engagement across information platforms, demanding greater transparency and promoting digital literacy can help ensure that users are exposed to diverse viewpoints and that their perspectives remain authentic.
