By Nick Clegg, President, Global Affairs at Meta.
As a company that’s been at the cutting edge of AI development for more than a decade, it’s been hugely encouraging to witness the explosion of creativity from people using our new generative AI tools, like our Meta AI image generator which helps people create pictures with simple text prompts.
As the difference between human and synthetic content gets blurred, people want to know where the boundary lies. People are often coming across AI-generated content for the first time and our users have told us they appreciate transparency around this new technology. So it’s important that we help people know when photorealistic content they’re seeing has been created using AI. We do that by applying “Imagined with AI” labels to photorealistic images created using our Meta AI feature, but we want to be able to do this with content created with other companies’ tools too.
That’s why we’ve been working with industry partners to align on common technical standards that signal when a piece of content has been created using AI. Being able to detect these signals will make it possible for us to label AI-generated images that users post to Facebook, Instagram and Threads. We’re building this capability now, and in the coming months we’ll start applying labels in all languages supported by each app. We’re taking this approach through the next year, during which a number of important elections are taking place around the world. During this time, we expect to learn much more about how people are creating and sharing AI content, what sort of transparency people find most valuable, and how these technologies evolve. What we learn will inform industry best practices and our own approach going forward.
A New Approach to Identifying and Labeling AI-Generated Content
When photorealistic images are created using our Meta AI feature, we do several things to make sure people know AI is involved, including putting visible markers that you can see on the images, and both invisible watermarks and metadata embedded within image files. Using both invisible watermarking and metadata in this way improves both the robustness of these invisible markers and helps other platforms identify them. This is an important part of the responsible approach we’re taking to building generative AI features.
Since AI-generated content appears across the internet, we’ve been working with other companies in our industry to develop common standards for identifying it through forums like the Partnership on AI (PAI). The invisible markers we use for Meta AI images – IPTC metadata and invisible watermarks – are in line with PAI’s best practices.
We’re building industry-leading tools that can identify invisible markers at scale – specifically, the “AI generated” information in the C2PA and IPTC technical standards – so we can label images from Google, OpenAI, Microsoft, Adobe, Midjourney, and Shutterstock as they implement their plans for adding metadata to images created by their tools.
AI Is Both a Sword and a Shield
Our Community Standards apply to all content posted on our platforms regardless of how it is created. When it comes to harmful content, the most important thing is that we are able to catch it and take action regardless of whether or not it has been generated using AI. And the use of AI in our integrity systems is a big part of what makes it possible for us to catch it.
We’ve used AI systems to help protect our users for a number of years. For example, we use AI to help us detect and address hate speech and other content that violates our policies. This is a big part of the reason why we’ve been able to cut the prevalence of hate speech on Facebook to just 0.01-0.02% (as of Q3 2023). In other words, for every 10,000 content views, we estimate just one or two will contain hate speech.
While we use AI technology to help enforce our policies, our use of generative AI tools for this purpose has been limited. But we’re optimistic that generative AI could help us take down harmful content faster and more accurately. It could also be useful in enforcing our policies during moments of heightened risk, like elections. We’ve started testing Large Language Models (LLMs) by training them on our Community Standards to help determine whether a piece of content violates our policies. These initial tests suggest the LLMs can perform better than existing machine learning models. We’re also using LLMs to remove content from review queues in certain circumstances when we’re highly confident it doesn’t violate our policies. This frees up capacity for our reviewers to focus on content that’s more likely to break our rules.
AI-generated content is also eligible to be fact-checked by our independent fact-checking partners and we label debunked content so people have accurate information when they encounter similar content across the internet.
Meta has been a pioneer in AI development for more than a decade. We know that progress and responsibility can and must go hand in hand. Generative AI tools offer huge opportunities, and we believe that it is both possible and necessary for these technologies to be developed in a transparent and accountable way. That’s why we want to help people know when photorealistic images have been created using AI, and why we are being open about the limits of what’s possible too. We’ll continue to learn from how people use our tools in order to improve them. And we’ll continue to work collaboratively with others through forums like PAI to develop common standards and guardrails.
