Experts say increasingly realistic campaign ads could make it harder for voters to distinguish authentic messages from fabricated ones
Deepfakes, Elections, and Shrinking the Liar’s Dividend
Heightened public awareness of the power of generative AI could give politicians an incentive to lie about the authenticity of real content.
AI Fakes Spread Disinformation. Is the Distrust They Create Even Worse?
Manipulated images undermine our shared reality—and the democracy built upon it.
Google research shows the fast rise of AI-generated misinformation | CBC News
From fake images of war to celebrity hoaxes, AI technology has spawned new forms of reality-warping misinformation online. New analysis co-authored by Google researchers shows just how quickly the problem has grown.
Republican AI Ad Uses Cutting Edge Tech to Tell Age Old Lies
A political ad made by the GOP in response to Biden's reelection announcement imagines a dystopia using AI-generated imagery.
How to tell if an image is AI-generated
Scammers are using AI-generated images to make fake stories more convincing. Here's how to separate real from fake.
Deep Storytelling: Collective Sensemaking and Layers of Meaning in U.S. Elections
Misinformation and disinformation about elections remain pressing concerns for researchers, policymakers, and the public. Critics, however, argue that fears surrounding these issues are exaggerated due to a lack of evidence of impact. This debate highlights the challenges inherent in assessing the impacts of misinformation, as the drivers of false and misleading content often exist in the context of a specific claim. To address this issue, we examined false and misleading information surrounding the 2020 and 2022 U.S. national elections, focusing on the contextual features of online conversations that fueled various rumors. We developed two qualitative codebooks, creating the second after realizing that the first, which labeled individual tweets, failed to capture broader rumoring dynamics. By integrating multi-layered qualitative coding with thematic analysis and quantitative visualizations, we show how influencers, political elites, and audiences collaboratively told deep stories from 2020 through 2022. As these stories were told, audiences interpreted events in 2022 through the lens of the 2020 story, guided by influencers' cues, leading to an evolution in storytelling style between the two election cycles. This ongoing performance was tailored to align with the incentive structures, affordances, and attention economy of social media. We combine deep stories with theories of collective sensemaking and rumoring, creating a framework to better assess the contextual features surrounding false and misleading information.
Deep Fakes: A Looming Challenge for Privacy, Democracy, and National Security — California Law Review
Harmful lies are nothing new. But the ability to distort reality has taken an exponential leap forward with “deep fake” technology. This capability makes it possible to create audio and video of real people saying and doing things they never said or did. Machine learning techniques are escalating th
People are trying to claim real videos are deepfakes. The courts are not amused
The unleashing of powerful, generative AI on the public is raising concerns that as the technology becomes more prevalent, it will become easier to claim that anything is fake.
The Liar’s Dividend: Can Politicians Claim Misinformation to Evade Accountability?
This study addresses the phenomenon of misinformation about misinformation, or politicians "crying wolf"' over fake news. Strategic and false claims that stories are fake news or deepfakes may benefit politicians by helping them maintain support after a scandal. We posit that this benefit, known as the "liar's dividend," may be achieved through two politician strategies: by invoking informational uncertainty or by encouraging oppositional rallying of core supporters. We administer five survey experiments to over 15,000 American adults detailing hypothetical politician responses to stories describing real politician scandals. We find that claims of misinformation representing both strategies raise politician support across partisan subgroups. These strategies are effective against text-based reports of scandals, but are largely ineffective against video evidence and do not reduce general trust in media. Finally, these false claims produce greater dividends for politicians than alternative responses to scandal, such as remaining silent or apologizing.
Deep Fakes: A Looming Challenge for Privacy
<p>Harmful lies are nothing new. But the ability to distort reality has taken an exponential leap forward with “deep fake” technology. This capability makes it possible to create audio and video of real people saying and doing things they never said or did. Machine learning techniques are escalating the technology’s sophistication, making deep fakes ever more realistic and increasingly resistant to detection. Deep-fake technology has characteristics that enable rapid and widespread diffusion, putting it into the hands of both sophisticated and unsophisticated actors.</p><p>While deep-fake technology will bring certain benefits, it also will introduce many harms. The marketplace of ideas already suffers from truth decay as our networked information environment interacts in toxic ways with our cognitive biases. Deep fakes will exacerbate this problem significantly. Individuals and businesses will face novel forms of exploitation, intimidation, and personal sabotage. The risks to our democracy and to national security are profound as well.</p><p>Our aim is to provide the first in-depth assessment of the causes and consequences of this disruptive technological change, and to explore the existing and potential tools for responding to it. We survey a broad array of responses, including: the role of technological solutions; criminal penalties, civil liability, and regulatory action; military and covert-action responses; economic sanctions; and market developments. We cover the waterfront from immunities to immutable authentication trails, offering recommendations to improve law and policy and anticipating the pitfalls embedded in various solutions.</p>
Deep Fakes: A Looming Challenge for Privacy
<p>Harmful lies are nothing new. But the ability to distort reality has taken an exponential leap forward with “deep fake” technology. This capability makes it possible to create audio and video of real people saying and doing things they never said or did. Machine learning techniques are escalating the technology’s sophistication, making deep fakes ever more realistic and increasingly resistant to detection. Deep-fake technology has characteristics that enable rapid and widespread diffusion, putting it into the hands of both sophisticated and unsophisticated actors.</p><p>While deep-fake technology will bring certain benefits, it also will introduce many harms. The marketplace of ideas already suffers from truth decay as our networked information environment interacts in toxic ways with our cognitive biases. Deep fakes will exacerbate this problem significantly. Individuals and businesses will face novel forms of exploitation, intimidation, and personal sabotage. The risks to our democracy and to national security are profound as well.</p><p>Our aim is to provide the first in-depth assessment of the causes and consequences of this disruptive technological change, and to explore the existing and potential tools for responding to it. We survey a broad array of responses, including: the role of technological solutions; criminal penalties, civil liability, and regulatory action; military and covert-action responses; economic sanctions; and market developments. We cover the waterfront from immunities to immutable authentication trails, offering recommendations to improve law and policy and anticipating the pitfalls embedded in various solutions.</p>
The Real Money In Modern ‘Journalism’ Now Involves Filling The Internet With ‘AI’-Generated Garbage
Last year both Gannett and Sports Illustrated were caught creating fake, “AI” generated journalists to create fake, plagiarism and mistake-prone “journalism.” In both instances …
Election Disinformation in Different Languages is a Big Problem in the U.S.
And it’s driving a wedge between voters in non-English communities Mis- and disinformation about elections predate the endless scroll of modern social media services. [1] Yet easy access to online information channels and amplification tools enable false narratives to spread at a massive scale. When false narratives are combined with data voids and unique cultural […]
Blackburn, Coons, Salazar, Dean, Colleagues Introduce Revised Version of NO FAKES Act
WASHINGTON, D.C. – Today, U.S. Senators Marsha Blackburn (R-Tenn.), Chris Coons (D-Del.), Thom Tillis (R-N.C.), and Amy Klobuchar (D-Minn.), along with U.S. Representatives Maria Salazar (R-Fla.) and Madeleine Dean (D-Penn.), introduced a revised version of their bipartisan Nurture Originals, Foster Art, and Keep Entertainment Safe (NO FAKES) Act to protect the voice and visual likenesses of individuals and creators from the proliferation of digital replicas created without their consent. Click here to read the updated bill text.
Brands using AI-generated influencers to promote products on social media
Investigation finds AI content that purports to show genuine customers, prompting calls for greater transparency
Are Large Language Models Sensitive to the Motives Behind Communication?
Human communication is $\textit{motivated}$: people speak, write, and create content with a particular communicative intent in mind. As a result, information that large language models (LLMs) and AI agents process is inherently framed by humans' intentions and incentives. People are adept at navigating such nuanced information: we routinely identify benevolent or self-serving motives in order to decide what statements to trust. For LLMs to be effective in the real world, they too must critically evaluate content by factoring in the motivations of the source---for instance, weighing the credibility of claims made in a sales pitch. In this paper, we undertake a comprehensive study of whether LLMs have this capacity for $\textit{motivational vigilance}$. We first employ controlled experiments from cognitive science to verify that LLMs' behavior is consistent with rational models of learning from motivated testimony, and find they successfully discount information from biased sources in a human-like manner. We then extend our evaluation to sponsored online adverts, a more naturalistic reflection of LLM agents' information ecosystems. In these settings, we find that LLMs' inferences do not track the rational models' predictions nearly as closely---partly due to additional information that distracts them from vigilance-relevant considerations. However, a simple steering intervention that boosts the salience of intentions and incentives substantially increases the correspondence between LLMs and the rational model. These results suggest that LLMs possess a basic sensitivity to the motivations of others, but generalizing to novel real-world settings will require further improvements to these models.