'Trust and Safety', 'Abuse Mitigation', and 'Content Moderation' are terms that are interchangeably used, frequently misunderstood, and often conflated. To successfully navigate the complexity and nuance of online safety, it is crucial to establish clear definitions of what these terms mean.
The emergence of generative AI has given rise to an alarming increase in AI-generated spam and abuse.
Products like ChatGPT, DALL-E, and GitHub Copilot have showcased remarkable content creation capabilities. Models underlying such technologies mirror their training material to craft content ranging from text, images, music, and code, often producing outcomes that can enhance many aspects of our lives. However, this very capability also opens the door to misuse, particularly in the generation of spam and abusive material.
On October 30, 2023, the Biden Administration issued an executive order proposing safeguards to mitigate concerns around AI-generated content. One such protection proposed instituting watermarking and other such indicators to denote the authenticity of content, with the intent of preventing deception and fraud.
One frequently debated question is whether platforms should be required to identify and flag generative AI content.
Like many such questions, the answer to this one depends entirely on context. Since the identification of generative AI content is only useful insofar as it mitigates harm, a more pertinent question to ask is: what new risks and potential for harm does generative AI content create? It is then worth asking: how can those risks be mitigated?
With AI-generated content rising, it has become vital to distinguish human-authored content from AI-generated impersonations across several contexts.
This is why we built Nuanced, a service for detecting AI-generated images. We help companies like dating apps, ad platforms, news sites, and marketplaces distinguish human-authored materials from AI-generated content.