274 citations
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38 papers
Experts Disagree on How to Fight AI Disinformation, but Agree That Health and Politics Need Different Solutions
Alexander Loth, Martin Kappes, Marc-Oliver Pahl
When 54 international experts assessed AI-generated disinformation threats, they revealed a surprising pattern: while video deepfakes received the highest average threat ratings in…
Can Humans Tell? A Dual-Axis Study of Human Perception of LLM-Generated News
Alexander Loth, Martin Kappes, Marc-Oliver Pahl
Can humans tell whether a news article was written by a person or a large language model (LLM)? We investigate this question using JudgeGPT, a study platform that independently mea…
Good for the Planet, Bad for Me? Intended and Unintended Consequences of AI Energy Consumption Disclosure
Michael Klesel, Uwe Messer
To address the high energy consumption of artificial intelligence, energy consumption disclosure (ECD) has been proposed to steer users toward more sustainable practices, such as c…
Eroding the Truth-Default: A Causal Analysis of Human Susceptibility to Foundation Model Hallucinations and Disinformation in the Wild
Alexander Loth, Martin Kappes, Marc-Oliver Pahl
As foundation models (FMs) approach human-level fluency, distinguishing synthetic from organic content has become a key challenge for Trustworthy Web Intelligence. This paper prese…
Industrialized Deception: The Collateral Effects of LLM-Generated Misinformation on Digital Ecosystems
Alexander Loth, Martin Kappes, Marc-Oliver Pahl
Generative AI and misinformation research has evolved since our 2024 survey. This paper presents an updated perspective, transitioning from literature review to practical counterme…
The Verification Crisis: Expert Perceptions of GenAI Disinformation and the Case for Reproducible Provenance
Alexander Loth, Martin Kappes, Marc-Oliver Pahl
The growth of Generative Artificial Intelligence (GenAI) has shifted disinformation production from manual fabrication to automated, large-scale manipulation. This article presents…