Descripción del proyecto
Una plataforma innovadora para desacreditar contenidos de desinformación
La desinformación supone una importante amenaza para las democracias modernas. Por ello, se necesitan nuevas soluciones para lograr desacreditar su contenido. El equipo del proyecto AI4Debunk, financiado con fondos europeos, pretende crear cuatro interfaces basadas en inteligencia artificial y centradas en el ser humano. Estas interfaces incluyen un complemento web, una plataforma colaborativa, una aplicación para teléfonos inteligentes y una interfaz de realidad aumentada (RA), que se construirán utilizando una interfaz de programación de aplicaciones (API). El equipo del proyecto se centrará en la desinformación sobre la guerra de Ucrania y el cambio climático, y mapeará el contenido de la desinformación en gráficos de conocimiento para luego compararlo con bases de datos existentes. La plataforma, llamada Disinfopedia, está diseñada para detectar al instante contenidos falsos. La aplicación verifica contenidos de la vida cotidiana, y la interfaz de RA ofrece información sobre noticias falsas y redes sociales.
Objetivo
"AI4Deunk aims to develop 4 human-centered AI-powered interfaces: a web plug-in, a collaborative platform, a smartphone app, and an AR interface. All these solutions will be built upon a ""debunking"" API.
To develop such an API, AI4Debunk will focus on 2 peculiar disinformation topics: the war in Ukraine and disinformation related to climate change. By deeply analysing these case studies thanks to sociological experts (targets groups, sources, way of diffusion...) we will map disinformation contents on knowledge graphs, and compare them to existing databases thanks to AI methods such as NLP. These knowledge graphs will be the skeleton of our technical work: one will be multimodal meaning that all types of modalities (written content, image, video) will be directly embedded, while the monomodal will be only about written content (the different modalities will be previously embedded into written content).
Then debunking modules will be developed, for each type of modality, based on AI and ML methods that could detect and examine questionable content, by comparing it to the context extracted from the knowledge graphs. RNN, CNN, multi-modal language interpretation, and transformers methods will be used to that extent. The goal of such modules is to give a score of fakeness, which we will call Disinofscore, for content given as input.
The API will then integrate this method and will be the bases of the 4 interfaces abovementioned. The plugin will allow people to be instantly noticed when a content they are considering is fake or true, the collaborative platform - also called Disinfopedia, will allow more proactive online users to report questionable content, that will be checked by a debunking committee chaired by a senior analyst, the app interface will allow people to check content in their everyday life, while the AR interface will allow citizens to have hindsight on the incoming generation of fake news and social media."
Ámbito científico
- natural sciencescomputer and information sciencesdatabases
- natural sciencescomputer and information sciencesknowledge engineering
- engineering and technologyelectrical engineering, electronic engineering, information engineeringinformation engineeringtelecommunicationsmobile phones
- natural sciencesearth and related environmental sciencesatmospheric sciencesclimatologyclimatic changes
Palabras clave
Programa(s)
Convocatoria de propuestas
HORIZON-CL4-2023-HUMAN-01-CNECT
Consulte otros proyectos de esta convocatoriaRégimen de financiación
HORIZON-IA - HORIZON Innovation ActionsCoordinador
1586 RIGA
Letonia