Involving humans in AI-supported fake news detection
Involving humans in AI-supported fake news detection
Amit Miller, M.Sc. student at the department of Industrial Advisor: Prof. Joachim Meyer
Abstract:
“Fake News” refers to false or misleading information presented as news, often spread online or through social media. It is typically created to manipulate public opinion, generate revenue through sensationalism, or promote specific agendas .An overwhelming amount of fake news is circulating online, making it increasingly difficult to distinguish truth from falsehood. This vast spread creates confusion, as fake news often mimics credible sources, blurring the line between real and fabricated information. Our research analyzes a system, such as a news desk at a media site, that receives a stream of incoming news items from various sources and has to decide which items to publish and which to discard as fake. To do so, one or more people can view items and there is also an AI system that classifies the incoming items. We evaluated various configurations within this hybrid setup with a computational model. We reviewed performance metrics in order to provide recommendations on the use of such a system as a function of properties of the AI and the human, the costs and benefits of publishing or not publishing fake or true items, and the relative frequency of fake items in the incoming stream. The results show that a clear recommendation can be given in most cases, and that some configurations are superior to others in the cases we reviewed. Additionally, we were able to show that some parameters can be combined to reduce the complexity of the problem.
Bio:
Amit Miller is a M.Sc. student in the department of industrial engineering in Tel Aviv university. He works as a professional in the gaming industry, using data, analytics & AI in order to optimize business decisions. He also holds a B.Sc. degree in Civil engineering from the Technion.
