Policies that outline what is and isn't allowed on the Facebook app.
Policies for ad content and business assets.
Other policies that apply to Meta technologies.
How we update our policies, measure results, work with others and more.
How we help prevent interference, empower people to vote and more.
How we work with independent fact-checkers, and more, to identify and take action on misinformation.
How we assess content for newsworthiness.
How we reduce problematic content in News Feed.
Quarterly report on how well we're doing at enforcing our policies in the Facebook app and on Instagram.
Report on how well we're helping people protect their intellectual property.
Report on government request for people's data.
Report on when we restrict content that's reported to us as violating local law.
Report on intentional Internet restrictions that limit people's ability to access the Internet.
Quarterly report on what people see on Facebook, including the content that receives the widest distribution during the quarter.
Download current and past regulatory reports for Facebook and Instagram.
JAN 26, 2022
Meta's technologies detect and remove the majority of violating content before it's ever reported. When someone posts on Facebook or Instagram, our technologies check to see if the content goes against the Facebook Community Standards and Instagram Community Guidelines. In most cases, identification is a simple matter. The post either clearly violates our policies or it doesn't.
Other times, identification is more difficult. Perhaps the sentiment of the post is unclear, its language is particularly complex or its imagery too context-dependent. In these cases, we conduct further review using people.
When determining which content our human review teams should review first, we consider three main factors:
How likely is it that the content could lead to harm, both online and offline?
How quickly is the content being shared?
How likely is it that the content in question does in fact violate our policies?
Because we want to prevent as much harm as possible, our review systems use technology to prioritise high-severity content with the potential for offline harm and viral content which is spreading quickly.
Our human review teams use their expertise in certain policy areas and locales to make difficult, often nuanced judgment calls. Every time reviewers make a decision, we use that information to train our technology. Over time, across millions of decisions, our technology gets better, allowing us to remove more violating content.
Like many machine-learning models, our technology improves over time as it receives more examples of violating content. This means that human review teams have been able to focus more on severe, viral, nuanced, novel and complex content – exactly the sort of decisions where people tend to make better decisions than technology.
Previously, human review teams would spend the vast majority of their time reviewing content reported by people. This meant that they were often spending too much time on low-severity or clearly non-violating content, and not enough time on the severest content with the greatest potential for harm. It also meant that many human decisions weren't that useful for improving our enforcement technology.
Our current approach to prioritisation addresses these issues, allowing us to review the most potentially harmful content first and improve our technology faster.
Not necessarily. Both human review teams and technology play a role in reviewing user reports. In cases where our technology can analyse a given piece of content, it will automatically take action – or not – on the content in question.
To address fairness and inclusion concerns associated with the deployment of AI in Meta technologies, we created our Responsible AI team – a dedicated, multidisciplinary team of ethicists, social and political scientists, policy experts, artificial intelligence researchers and engineers. The team's overall goal is to develop guidelines, tools and processes to tackle issues of AI responsibility and help ensure that these systemic resources are widely available across Meta.