Facebook and Instagram, used by billions of people daily, have a significant impact on users’ lives. Meta has today shared how they employ AI systems to curate content tailored to your preferences.
AI predictions play a pivotal role in determining content recommendations on Facebook and Instagram. The AI systems anticipate the value a piece of content holds for users, enabling earlier and more accurate content display.
Various predictions, including behavioral indicators and user feedback obtained through surveys, are utilized in combination to deliver content most relevant to users.
Introducing system cards, Meta provides accessible information to users without deep technical knowledge.
Currently, 22 system cards have been released, shedding light on the content ranking process, predictive models, and customization controls for Facebook and Instagram.
These system cards encompass various platforms such as Feed, Stories, Reels, and recommendations from accounts users do not follow.
To provide further granularity, Meta shares the signal categories and predictive models employed in Facebook’s Feed ranking. These signals encompass the majority of factors used to determine content relevance.
While Meta strives for transparency, there are limits to disclosure. Balancing the need for transparency with security concerns, Meta refrains from sharing signals that could be exploited to circumvent defenses against undesirable content.
In the coming weeks, Meta will introduce the Meta Content Library and API, a suite of research tools.
These tools provide comprehensive access to publicly-available content on Facebook and Instagram, catering to academic and research institutions pursuing scientific or public interest research topics.
