Descrizione del progetto
Un’innovativa piattaforma per smentire i contenuti di disinformazione
La disinformazione rappresenta una minaccia significativa per le democrazie moderne, motivo per cui sono necessarie nuove soluzioni al fine di smentire con successo i suoi contenuti. Il progetto AI4Debunk, finanziato dall’UE, mira a creare quattro interfacce alimentate dall’intelligenza artificiale e incentrate sugli esseri umani contenenti strumenti quali un plug-in web, una piattaforma collaborativa, un’applicazione per smartphone e un’interfaccia di realtà aumentata, tutti costruiti utilizzando un’interfaccia di debunking per programmi applicativi. Il progetto si concentrerà sulla disinformazione generata in relazione alla guerra in Ucraina e ai cambiamenti climatici, mappando i relativi contenuti su grafi della conoscenza e raffrontandoli con i database esistenti. La piattaforma, denominata Disinfopedia, è progettata per individuare istantaneamente i contenuti falsi: l’app verifica i contenuti della vita di tutti i giorni, mentre l’interfaccia basata sulla realtà aumentata fornisce informazioni sulle fake news e sui social media.
Obiettivo
"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."
Campo scientifico
- 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
Parole chiave
Programma(i)
Meccanismo di finanziamento
HORIZON-IA - HORIZON Innovation ActionsCoordinatore
1586 RIGA
Lettonia