A game changer for misinformation: The rise of generative AI

The year 2023 will be mentioned in history books as the point in time when advances in artificial intelligence became everyday news – everywhere. The decision by OpenAI to offer the general public access to their deep learning-based Generative Pre-trained Transformer (GPT) model opened up a floodgate of experimentation by journalists, designers, developers, teachers, researchers and artists.

Generative AI systems such as these have the ability to produce highly realistic synthetic text, images, video and audio – including fictional stories, poems, and programming code – with little to no human intervention. The combination of increased accessibility, sophistication and capabilities for persuasion may very well supercharge the dynamics of climate mis- and disinformation.

Accessibility

Accessibility refers to the fact that generative AI tools that produce highly realistic synthetic content, that is not necessarily accurate, are rapidly becoming easily available.

While the most capable models remain either private or behind monitorable application programming interfaces (APIs), some advanced models are publicly accessible, either in open code repositories or through public APIs including OpenAI, Google, Microsoft and Hugging Face (Table). In addition, the open source community has quickly managed to create much smaller versions of large language models that are almost equally powerful, and can be run on laptops or even phones (Dickson, 2023). All of these tools can, in principle, be prompted to deliberately generate false information, both in the form of text (McGuffie and Newhouse 2020; Buchanan et al. 2021) and photo-realistic but fake images (Mansimov et al., 2016; Goldstein et al., 2023).

Sophistication

Sophistication refers to how sophisticated AI-generated mis- and disinformation is. Social media users are more literate than sometimes assumed, and are able to detect and pushback on too simplistic mis- and disinformation tactics (Jones-Jang et al., 2021).

There is no need to utilize advanced AI to coordinate a disinformation campaign of course. For example, simply cut-and-pasting misinformation content to push a certain hashtag and issue online does not require advanced AI applications - especially if the text is short. Generative AI however, can easily create longer pieces of synthetic text, like blog posts and authoritative sounding articles. Such texts can be generated by including more specific prompts. Ben Buchanan and colleagues (2021) for example, tested the ability of GPT-3 to reproduce headlines in the style of the disreputable newspaper The Epoch Times, simply providing a couple of real headlines from the newspaper as prompts. In a more advanced test, the team managed to generate convincing news stories with sensationalist or clearly biased headlines, and also generate messages with the explicit intention to amplify existing social divisions (Buchanan et al., 2021). 

But text is not the only type of synthetic media that has become increasingly sophisticated lately. The increased sophistication of synthetic video and voice is also likely to create new mis- and disinformation challenges, although such tools are not publicly available yet. Using satire for political campaigning seems to form the frontier for what has become known as “deepfakes”- highly convincing video and audio that has been altered and manipulated to misrepresent someone as doing or saying something that was not actually done or said.

Digital artist Bill Posters collaborated with anonymous Brazilian activists to create a fake promotional video that shows Amazon CEO Jeff Bezos announcing his future commitment to protecting the Amazon rainforest on the occasion of the company’s 25th anniversary (Gregory and Cizek, 2023). In the 2018 Belgian Election, a deep fake video of former U.S. president Trump calling on the country to exit the Paris climate agreement was widely distributed despite the video’s poor quality.

Generative AI can also result in increasingly sophisticated tactics (Goldstein et al., 2023). Tactics that previously were computationally too heavy and manually expensive, become suddenly possible. Anyone with enough resources can increase the scalability of a disinformation operation by replacing human writers (or at least some tedious writing tasks) with language models. Flooding social media platforms with a diversity of messages promoting one specific narrative (say, false rumors about climate scientists manipulating data for an upcoming IPCC-report), is one possible application.

Generating personalized content for chatbots that engage with users in real-time becomes practically possible at scale with limited human manpower. Human-like messages including long-form content like news articles can be crafted, adapted to specific audiences (for example based on demographic information, or known political preferences), and the language tweaked continuously in ways that make disinformation attempts much more difficult to detect (examples from Goldstein et al., 2023).

Persuasion

Influencing public opinion is a matter of persuasion. False digital information and destructive narratives like conspiracy theories are problematic, but will only have tangible impacts on perceptions, opinions and behavior if they manage to actually persuade a reader. Persuasion is harder than simple amplification of a message, and requires well-formed and well-tailored arguments to be effective (Buchanan et al., 2021: 30). Large language models integrated in AI agents like Cicero, are already today able to engage in elaborate conversations and dialogue with humans in highly persuasive ways (FAIR et al., 2022).

One of the strengths of generative AI models is their ability to automate the generation of content that is “as varied, personalized, and elaborate as human-generated content”. Such content would go undetected with current bot detection tools, which rely for example on detecting identical repeated messages. It also allows small groups to make themselves look much larger online than they actually are (Goldstein et al., 2023).

Recent experimental studies indicate that AI-generated messages were as persuasive as human messages. To some extent, AI-generated messages were even perceived as more persuasive (i.e, more factual and logical) than those produced by humans, even on polarized policy issues (Bai et al., 2023). Kreps and colleagues (2022) note that individuals are largely incapable of distinguishing between AI- and human-generated text, but could not find evidence that AI-generated texts are able to shift individuals’ policy views. Jakesch and colleagues (2023) however, note that people tend to use simple heuristics to differentiate human- from AI-generated text. For example, people often associate first-person pronouns, use of contractions, or family topics with text produced by humans. As a result, it is easy to exploit such heuristics to produce text that is “more human than human.”

To what extent generative AI will allow persuasion at scale is too early to assess. But the landscape is changing rapidly. In April 2023, the organization NewsGuard identified 49 websites that appear to be created using generative AI and designed to look like typical news websites in seven languages — Chinese, Czech, English, French, Portuguese, Tagalog, and Thai.