The obligation to embed invisible watermarks or metadata should not extend to content created outside of AI generators.
Synthetic media tools are now able to produce images of real-life events and convincing audio of individuals with limited input data, and at scale. Generative AI tools are increasingly multimodal, with text, image, video, audio and code functioning interchangeably as input or output
Given the lack of public understanding of AI, the rapidly increasing verisimilitude of audiovisual outputs, and the absence of robust transparency and accountability, generative AI is also deepening distrust of both specific items of content as well as broader ecosystems of media and information.
In this blog post, we explore how legislators can center human rights by drawing from the thinking that many human rights organizations and professionals have advanced with regard to transparency, privacy, and provenance in audiovisual content.
Among other proposals, the framework proposes that AI system providers ‘watermark or otherwise provide technical disclosures of AI-generated deepfakes’.
The AI Labeling Act introduced in the US Senate in July by Senators Brian Schatz (D-HI) and John Kennedy (R-LA) is the most detailed proposal regarding transparency in AI generated output to date.
The Act puts forward constructive steps towards the standardization of the ‘how’ of media production (instead of focusing on the identity of the creator), and a more responsible AI pipeline that includes some elements of upstream and downstream accountability.
Despite these positive notes, uncertainties remain about how a visible labeling requirement that is not specific to a subset of content, like political advertising, could be applied across the board in a meaningful way or without unintended consequences. For instance, the Act says that the disclaimer should be ‘clear and conspicuous’ when the content is AI generated or edited in a way that ‘materially alter[s] the meaning or significance that a reasonable person would take away’.
The proposal also omits any direct references to the language of the disclaimer, missing the opportunity for a more inclusive technology policy that is grounded in the realities of how digital content spreads.
More importantly, the viability of generalized ‘clear and conspicuous’ disclosures over time is unproven and unclear.
These could all ‘materially alter the meaning or significance that a reasonable person would take away from the content’ and therefore meet the labeling obligation. In these instances, simply indicating the presence of AI may not be that helpful without further explanation of how it is used and which part of the artifact is synthetic.
. Lastly, most visible labels are easily removable. This could happen even without deceptive intent–essentially leaving it to the users to decide whether they think they are materially altering the meaning of a piece of media, but making developers and providers liable for these decisions.
We will hence require systems that explain both the AI-based origins or production processes used in producing a media artifact, but also document non-synthetic audio or visual content generated by users and other digital processes. It will be hard to address AI content in isolation from this broader question of media provenance.
Visible signals or labels can be useful in specific scenarios such as AI-based imagery or production within election advertising. However, visible watermarks are often easily cropped, scaled out, masked or removed, and specialized tools can remove them without leaving a trace.
Labels also bring up questions about their interpretability and accessibility by different audiences, from the format of the label, to their placement or language they employ.
According to Everypixel Journal, more than 11 billion images have been created using models from three open source repositories. In these situations, invisible watermarks can be removed by deleting the line that generates it. Promising research by Meta on Stable Signature roots the watermark in the model and allows tracing the image back to where it was created, even being able to deal with various versions of the same model.
Microsoft has been working on implementing provenance data on AI content using C2PA specifications, and Adobe has started to provide it via its Content Credentials approach. These methods can allow people to understand the lifecycle of a piece of content, from its creation or capture to its production and distribution
Cryptographic signature and provenance-based standards track the production process of content over time, and enable the reconnection of a piece of content to a set of metadata if that is removed.
Legislators should also consider situations in which a user inadvertently removes the metadata from a piece of content, and how this provenance approach fares vis-à-vis existing systems for content distribution (for instance many social media platforms strip the metadata prior to publication for privacy and security reasons).
However, they struggle with scalability and are not yet interoperable across watermarking and detection techniques–without standardization, watermarks created by an image generation model may not be detected confidently enough by a content distribution platform,
Glasp is a social web highlighter that people can highlight and organize quotes and thoughts from the web, and access other like-minded people’s learning.