To improve Meta’s ability to remove non-violating content from banks programmed to identify or automatically remove violating content, Meta should ensure that content with high rates of appeal and high rates of successful appeal is re-assessed for possible removal from its Media Matching Service banks. The Board will consider this recommendation implemented when Meta: (i) discloses to the Board the rates of appeal and successful appeal that trigger a review of Media Matching Service-banked content, and (ii) confirms publicly that these reassessment mechanisms are active for all its banks that target violating content.
Our commitment: We will implement this recommendation using a gradual approach based on the complexity of the governance, enforcement and maturity levels of individual banks. Some banking teams are already implementing this recommendation, while others are newer and still in the process of training their auditing systems. Across all Media Matching Service (MMS) banks, we plan to implement product and governance innovations to more effectively and efficiently remove incorrectly banked content.
Considerations: MMS banks are a collection of “hashed” content, used primarily to detect and take scaled action on media (e.g. image, video) across Facebook and Instagram that violates Meta’s Community Standards. Content identified as violating is stored in banks to detect additional occurrences across Facebook and Instagram for scaled enforcement. In other cases, non-violating content is banked to prevent it from being removed and increase review capacity. Banks can be configured to either only detect and take action on newly uploaded content or to scan existing content on the platform.
We regularly evaluate the performance of MMS banks in correctly identifying violating content. For example, if bank precision declines, as often measured through user feedback, we conduct analyses to understand the root cause and identify solutions, including technical issue fixes and updates to reviewer training. We also measure the accuracy of the reviewers who determine where content should be banked.
These quality assessments are often conducted at the individual bank level and not always based on appeal rates. Because there is no single banking team that can implement a uniform solution to this recommendation across all the areas where banking occurs at Meta, implementing this recommendation will require a gradual approach based on the complexity of the governance and maturity levels of individual banks.
There are automated systems and alerts that run across most banks to detect anomalies or spikes in appeal rates. Spike detection systems also allow us to automatically limit false positives generated by incorrectly banked content. Teams review and investigate these alerts to make future banking more accurate. When anomaly alerts catch clusters of content that trigger a high number of appeals, they send notifications to our Global Operations teams which review and ultimately remove any incorrectly-banked content from enforcement banks. For content that is non-violating, these teams also move that content to “ignore” banks to prevent future false positives.
Some of our banking teams are working on a new automated tool to detect whether precision issues are stemming from technical mistakes (e.g. algorithm false positives) or human error (e.g. a reviewer entering inaccurate content). As a result of this recommendation and our ongoing commitment to banking accuracy, more teams are likely to launch this tool in the near future. Similarly, we expect more banking teams to adopt an “appeals circuit breaker” feature which pauses a cluster of content with a small number of successful appeals and a reasonable ratio of successful to unsuccessful appeals so that the content can be re-reviewed and removed from banks as appropriate. Teams that do not already have these features in place are developing pipelines to flag this type of content for re-review. In line with this recommendation, we are also in the early stages of testing and rolling out a “hygiene sweep" capability based on appeals signals. This tool will allow us to better identify clear patterns in the root cause of false positives. We will provide further updates on our progress in future Quarterly Updates.