QRE 18.1.2
Relevant Signatories will publish the main parameters of their recommender systems, both in their report and, once it is operational, on the Transparency Centre.
As mentioned in previous reports,
Facebook system cards help people understand
how AI shapes their product experiences and provides insights into how the Feed ranking system dynamically works to deliver a personalised experience on Facebook.
These cards provide detail on how our systems work in a way that is accessible for those who don’t have deep technical knowledge. In June 2023, we released 14 system cards for Facebook. There are 15 system cards for Facebook which are periodically updated. They give information about how our AI systems rank content, some of the predictions each system makes to determine what content might be most relevant, as well as the controls users can use to help customise users' experience. They cover Feed, Stories, Reels and other surfaces where people go to find content from the accounts or people they follow. The system cards also cover AI systems that recommend “unconnected” content from people, groups, or accounts they don’t follow. A more detailed explanation of the AI behind content recommendations is available
here.
To give a further level of detail beyond what’s published in the system cards, we have shared the types of inputs – known as signals – as well as the predictive models these signals inform that help determine what content users may find most relevant from their network on Facebook. Users can find these signals and predictions in the
Transparency Centre, along with how frequently they tend to be used in the overall ranking process.
We also use signals to help identify harmful content, which we remove as we become aware of it, as well as to help reduce the distribution of other types of problematic or low-quality content in line with our Content Distribution Guidelines.