- Joined
- Nov 22, 2021
This is a paper talking about provenance and trying to have sites differentiate between AI-generated and human made posts.
Some excerpts:
If you thought two-factor authentication was something, this should be even more wild. Is the age of the digital human ID just over the horizon in the next few years?
PDF in attachments
Some excerpts:
In recent years, artificial intelligence (AI) systems have significantly improved and their capabilities have expanded. In particular, AI systems called “generative models” have made great progress in automated content creation, such as images generated from text prompts. One area of particularly rapid development has been generative models that can produce original language, which may have benefits for diverse fields such as law and healthcare
Once models are built, developers can choose how users interact with them. AI providers have some actions available to them that might reduce bad actors’ access to generative language models. At the same time, these actions could be highly costly for organizations looking to commercialize their models and would require large amounts of cooperation across all relevant AI providers to ensure that propagandists could not simply gravitate toward other equally capable models without similar restrictions in place
Because technical detection of AI-generated text is challenging, an alternate approach is to build trust by exposing consumers to information about how a particular piece of content is created or changed. Tools such as phone cameras or word processing software could build the means for content creators to
track and disclose this information. In turn, social media platforms, browsers, and internet protocols could publicize these indicators of authenticity when a user interacts with content.
This intervention requires a substantial change to a whole ecosystem of applications and infrastructure in order to ensure that content retains indicators of authenticity as it travels across the internet. To this end, the Coalition for Content Provenance and Authenticity (C2PA) has brought together software application vendors, hardware manufacturers, provenance providers, content publishers, and social media platforms to propose a technical standard for content provenance that can be implemented across the internet. This standard would provide information about content to consumers, including its date of creation, authorship, hardware, and details regarding edits, all of which would be validated with cryptographic signatures
Developers might choose to restrict model access to only trusted institutions, such as known companies and research organizations, and not to individuals or governments likely to use their access to spread disinformation. Huawei initially appears to have intended an access regime along these lines for its PanGu-α model
The preceding mitigations address the supply of AI-generated misinformation. However, as long as target audiences remain susceptible to propaganda that aligns with their beliefs, there will remain an incentive for influence operations generally, as well as incentives more specifically for propagandists to leverage AI to make those operations more effective. In this section, we therefore discuss two interventions that might help address the demand side of the misinformation problem: media literacy campaigns, and the use of AI tools to aid media consumers in interpreting and making informed choices about the information they receive.
Just as generative models can be used to generate propaganda, they may also be used to defend against it. Consumer-focused AI tools could help information consumers identify and critically evaluate content or curate accurate information. These tools may serve as an antidote to influence operations and could reduce the demand for disinformation. While detection methods (discussed in Section 5.2.1) aim to detect whether content is synthetic, consumer-focused tools instead try to equip consumers to make better decisions when evaluating the content they encounter.
Possibilities for such tools are numerous. Developers could produce browser extensions and mobile applications that automatically attach warning labels to potential generated content and fake accounts, or that selectively employ ad-blockers to demonetize them. Websites and customizable notification systems could be built or improved with AI-augmented vetting, scoring, and ranking systems to organize, curate, and display user-relevant information while sifting out unverified or generated sources. Tools and built-in search engines that merely help users quickly contextualize the content they consume could help their users evaluate claims, while lowering the risk of identifying true articles as misinformation. Such “contextualization engines” may be especially helpful in enabling users to analyze a given source and then find both related high-quality sources and areas where relevant data is missing. By reducing the effort required to launch deeper investigations, such tools can help to align web traffic revenue more directly with user goals, as opposed to those of advertisers or influence operators. Another proposal suggests using AI-generated content to educate and inoculate a population against misleading beliefs.
If you thought two-factor authentication was something, this should be even more wild. Is the age of the digital human ID just over the horizon in the next few years?
PDF in attachments
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