AI-based-technologies for trustworthy solutions against disinformation

Leistungen

The consortium will agree upon the exact design of the set of data-driven, model-driven and AI-driven tools that will be tailored and integrated between themselves to specifically serve the needs of the AI4TRUST platform and ecosystem.

Pilot planning report

This deliverable will illustrate the detailed piloting requirements, planning, and KPI definition.

AI4TRUST Platform Specification

This deliverable will provide the system specifications as detailed in T5.1. The initial release will focus on the most relevant components and will be progressively updated to reflect the evolution of the AI4TRUST APIs.

Explainability and Transparency report and AI tools

This deliverable will report results of T4.1-T4.2 to outline the explainability and transparency needs for the AI4TRUST platform and the socio-linguistic, user contextual, and social network factors of design for disinformation and mitigating misinformation. It will also include the first version of Trustworthy AI algorithms produced in T4.3.

First report on social dynamics of misinformation

This deliverable will report the first version of the findings of T4.1 on the socio-contextual basis for misinformation.

AI4TRUST Communication and Dissemination Plan

Strategic document setting the communication and dissemination strategy of the project, including key indicators, roles and responsibilities.

First release of AI tools for disinformation detection

This deliverable will include the first release of T3.1-T3.4 and will also report on the exposed APIs for the integration of some of these technologies in the AI4TRUST platform.

Ground truth data and social listening data streams

The access to ground data collected by the Human Fact-Checking network, as well as the one collected from social listening data streams, will be provided, in the earliest stages of the project to all technical members of the consortium to bootstrap the R&D.

Veröffentlichungen

Style-Hallucinated Dual Consistency Learning: A Unified Framework for Visual Domain Generalization

Autoren: Zhao Y.; Zhong Z.; Zhao N.; Sebe N.; Lee G. H.
Veröffentlicht in: International Journal of Computer Vision, 2023, ISSN 0920-5691
Herausgeber: Kluwer Academic Publishers
DOI: 10.48550/arxiv.2212.09068

Cross-modal Networks, Fine-Tuning, Data Augmentation and Dual Softmax Operation for MediaEval NewsImages 2023

Autoren: A. Leventakis, D. Galanopoulos, V. Mezaris
Veröffentlicht in: CEUR Workshop Proceedings, 2024, ISSN 1613-0073
Herausgeber: CEUR

Countering Misinformation via Emotional Response Generation

Autoren: Russo, Daniel; Kaszefski-Yaschuk, Shane; Staiano, Jacopo; Guerini, Marco
Veröffentlicht in: EMNLP, 2023
Herausgeber: ACL
DOI: 10.48550/arxiv.2311.10587

Benchmarking the Generation of Fact Checking Explanations

Autoren: Russo, Daniel; Tekiroğlu, Serra Sinem; Guerini, Marco
Veröffentlicht in: TACL, 2023, ISSN 2307-387X
Herausgeber: ACL
DOI: 10.48550/arxiv.2308.15202

Leistungen

Veröffentlichungen

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