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CORDIS

AI-CODE - AI services for COntinuous trust in emerging Digital Environments

CORDIS provides links to public deliverables and publications of HORIZON projects.

Links to deliverables and publications from FP7 projects, as well as links to some specific result types such as dataset and software, are dynamically retrieved from OpenAIRE .

Deliverables

Prototyping the disinformation detection and next generation of social media services (opens in new window)

Initial versions of the disinformation detection services focusing on next generation social media, as carried out in T4.1

Prototype version of Dynamic Simulator for Media Professionals (opens in new window)

Initial -prototype of the interactive simulator for media professionals allowing users to observe selected functionalities of the simulator and give feedback regarding its design.

Prototyping the Media Asset Annotation and Management (MAAM) service (opens in new window)

Initial versions of services that were built and prototyped in T4.3.

Prototype version of the Personal Companion for media professionals service (opens in new window)

Initial version of the Personal Companion for understanding disinformation for media professionals service.

Prototyping the Trustability and credibility assessment services (opens in new window)

First prototype of the Trustability and credibility assessment services produced in T4.2

Prototyping transparency services for AI-model cards (opens in new window)

Initial versions of transparency services for AI-model cards carried out in T6.2

Quality Plan (opens in new window)

This deliverable will state all the quality procedures to guarantee a successful project implementation.

Adaptation of existing credibility and trust assessment and disinformation detection tools to next-generation social media and AI-generated content (opens in new window)

On the basis of T2.4, this deliverable will complement previous two deliverables and provide recommendations for development of AI-CODE services.

Data Management Plan (opens in new window)

This deliverable will describe how the AI-CODE project manages data throughout its life cycle, in order to be compliant to the regulatory framework.

Functional specifications and technical requirements (opens in new window)

Description of the necessary functionalities and technical requirements of the AI-CODE services based on results from tasks T3.3 und T3.4.

Report on the assessment of the role of novel text and image generative AI methodologies in producing and combating disinformation (opens in new window)

This deliverable will summarise the research performed in T2.2 and T2.3 and describe positive as well as negative consequences of generative AI adopted in social media.

Impact master plan (opens in new window)

Document detailing the project dissemination, and communication plan, outlining the target groups and relevant tailored outreach strategies and the project communication channels.

User-centred requirements definition (opens in new window)

Requirements definition for the defined user groups and use case scenarios based on results of the analysis in T3.1, T3.2 and T3.4.

Report on technological and socio-behavioural characterization of next-generation social media (opens in new window)

This deliverable will summarise the research performed in T2.1 and outline the emerging trust and disinformation risks.

Publications

LLMs vs Established Text Augmentation Techniques for Classification: When do the Benefits Outweight the Costs? (opens in new window)

Author(s): Čegiň, Ján; Simko, Jakub; Brusilovsky, Peter
Published in: Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) (NAACL 2025), 2025
Publisher: Association for Computational Linguistics
DOI: 10.48550/ARXIV.2408.16502

Effects of diversity incentives on sample diversity and downstream model performance in LLM-based text augmentation (opens in new window)

Author(s): Jan Cegin, Branislav Pecher, Jakub Simko, Ivan Srba, Maria Bielikova, Peter Brusilovsky
Published in: Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2024
Publisher: Association for Computational Linguistics
DOI: 10.18653/v1/2024.acl-long.710

Authorship Obfuscation in Multilingual Machine-Generated Text Detection (opens in new window)

Author(s): Macko, Dominik; Moro, Robert; Uchendu, Adaku; Srba, Ivan; Lucas, Jason Samuel; Yamashita, Michiharu; Tripto, Nafis Irtiza; Lee, Dongwon; Simko, Jakub; Bielikova, Maria
Published in: Findings of the Association for Computational Linguistics: EMNLP 2024, 2024
Publisher: Association for Computational Linguistics
DOI: 10.48550/arXiv.2401.07867

SIDBench: A Python framework for reliably assessing synthetic image detection methods (opens in new window)

Author(s): Manos Schinas, Symeon Papadopoulos
Published in: 3rd ACM International Workshop on Multimedia AI against Disinformation, 2024
Publisher: ACM
DOI: 10.1145/3643491.3660277

ModaFact: Multi-paradigm Evaluation for Joint Event Modality and Factuality Detection

Author(s): Rovera, Marco; Cristoforetti, Serena; Tonelli,Sara
Published in: Proceedings of the 31st International Conference on Computational Linguistics, 2025
Publisher: Association for Computational Linguistics

A Ship of Theseus: Curious Cases of Paraphrasing in LLM-Generated Texts (opens in new window)

Author(s): Tripto, Nafis Irtiza; Venkatraman, Saranya; Macko, Dominik; Moro, Robert; Srba, Ivan; Uchendu, Adaku; Le, Thai; Lee, Dongwon
Published in: Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2024
Publisher: Association for Computational Linguistics
DOI: 10.48550/arXiv.2311.08374

MultiSocial: Multilingual Benchmark of Machine-Generated Text Detection of Social-Media Texts (opens in new window)

Author(s): Dominik Macko, Jakub Kopál, Robert Moro, Ivan Srba
Published in: Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2025
Publisher: Association for Computational Linguistics
DOI: 10.48550/ARXIV.2406.12549

KInIT at SemEval-2024 Task 8: Fine-tuned LLMs for Multilingual Machine-Generated Text Detection (opens in new window)

Author(s): Spiegel, Michal; Macko, Dominik
Published in: Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024), 2024
Publisher: Association for Computational Linguistics
DOI: 10.48550/arXiv.2402.13671

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