Emerging types of generative AI misuse, which aren’t overtly malicious, still raise ethical concerns. For instance, latest types of political outreach are blurring the lines between authenticity and deception, reminiscent of government officials suddenly speaking quite a lot of voter-friendly languages without transparent disclosure that they’re using generative AI, and activists using the AI-generated voices of deceased victims to plead for gun reform.
While the study provides novel insights on emerging types of misuse, it’s value noting that this dataset is a limited sample of media reports. Media reports may prioritize sensational incidents, which in turn may skew the dataset towards particular varieties of misuse. Detecting or reporting cases of misuse might also be tougher for those involved because generative AI systems are so novel. The dataset also doesn’t make a direct comparison between misuse of generative AI systems and traditional content creation and manipulation tactics, reminiscent of image editing or organising ‘content farms’ to create large amounts of text, video, gifs, images and more. Up to now, anecdotal evidence suggests that traditional content manipulation tactics remain more prevalent.
Staying ahead of potential misuses
Our paper highlights opportunities to design initiatives that protect the general public, reminiscent of advancing broad generative AI literacy campaigns, developing higher interventions to guard the general public from bad actors, or forewarning people and equipping them to identify and refute the manipulative strategies utilized in generative AI misuse.
This research helps our teams higher safeguard our products by informing our development of safety initiatives. On YouTube, we now require creators to share when their work is meaningfully altered or synthetically generated, and seems realistic. Similarly, we updated our election promoting policies to require advertisers to reveal when their election ads include material that has been digitally altered or generated.
As we proceed to expand our understanding of malicious uses of generative AI and make further technical advancements, we comprehend it’s more vital than ever to be sure that our work isn’t happening in a silo. We recently joined the Content for Coalition Provenance and Authenticity (C2PA) as a steering committee member to assist develop the technical standard and drive adoption of Content Credentials, that are tamper-resistant metadata that shows how content was made and edited over time.
In parallel, we’re also conducting research that advances existing red-teaming efforts, including improving best practices for testing the protection of huge language models (LLMs), and developing pioneering tools to make AI-generated content easier to discover, reminiscent of SynthID, which is being integrated right into a growing range of products.
In recent times, Jigsaw has conducted research with misinformation creators to grasp the tools and tactics they use, developed prebunking videos to forewarn people of attempts to control them, and shown that prebunking campaigns can improve misinformation resilience at scale. This work forms a part of Jigsaw’s broader portfolio of knowledge interventions to assist people protect themselves online.
By proactively addressing potential misuses, we are able to foster responsible and ethical use of generative AI, while minimizing its risks. We hope these insights on probably the most common misuse tactics and techniques will help researchers, policymakers, industry trust and safety teams construct safer, more responsible technologies and develop higher measures to combat misuse.
