This extended abstract was presented at the International Conference on Computational Social Science (IC2S2) 2022.
In the past few years, there has been an increase in AI-based disinformation campaigns, which are attempts to spread misinformation online for strategic reasons. How AI-systems explain how they arrive at their classifications can be deceptive, in that they can be manipulated to make the system appear more reliable than it is. For example, a bot may claim to be human in order to evade detection, or a machine learning system may falsely claim a piece of information to be true when it is not. While previous work has shown that AI-explanations help people determine the veracity of information online and change people’s beliefs, little is known about how susceptible people are to deceptive AI systems.
This project investigates how people's discernment varies when AI systems are perceived as either human fact-checkers or AI fact-checking systems, and when explanations provided by those fact-checkers are either deceptive (i.e. the AI system falsely generating explanations for why a true headline is false or why a false headline is true) or honest (i.e. the AI system accurately generating explanations for why a true headline is true or why a false headline is false).
In a pilot study, we generated a dataset with honest and deceptive explanations for why a news headline was either true or false by prompting the state-of-the-art text-generation model GPT-3.