Gen AI bolsters enforcement

Our safety teams have long used machine learning powered by AI to enforce our policies at scale. It’s how, for years, we’ve been able to detect and block billions of bad ads before a person ever sees them. But, while still highly sophisticated, these machine learning models have historically needed to be trained extensively — they often rely on hundreds of thousands, if not millions of examples of violative content.

LLMs, on the other hand, are able to rapidly review and interpret content at a high volume, while also capturing important nuances within that content. These advanced reasoning capabilities have already resulted in larger-scale and more precise enforcement decisions on some of our more complex policies. Take, for example, our policy against Unreliable Financial Claims which includes ads promoting get-rich-quick schemes. The bad actors behind these types of ads have grown more sophisticated. They adjust their tactics and tailor ads around new financial services or products, such as investment advice or digital currencies, to scam users.

To be sure, traditional machine learning models are trained to detect these policy violations. Yet, the fast-paced and ever-changing nature of financial trends make it, at times, harder to differentiate between legitimate and fake services and quickly scale our automated enforcement systems to combat scams. LLMs are more capable of quickly recognizing new trends in financial services, identifying the patterns of bad actors who are abusing those trends and distinguishing a legitimate business from a get-rich-quick scam. This has helped our teams become even more nimble in confronting emerging threats of all kinds.

We’ve only just begun to leverage the power of LLMs for ads safety. Gemini, launched publicly last year, is Google’s most capable AI modeI. We’re excited to have started bringing its sophisticated reasoning capabilities into our ads safety and enforcement efforts.