The Weaponization of Generative AI and Domestic Information Manipulation: A Comparative Analysis of Electoral FIMI in Guatemala, Ecuador, and Honduras (2023–2025)
This paper analyzes the systemic threat of election-related Foreign Information Manipulation and Interference (FIMI) and Generative Artificial Intelligence (GAI) across recent Latin American electoral cycles in Guatemala (2023), Ecuador (2025), and Honduras (2025). Integrating the FIMI behavioral taxonomy with the DISARM framework and systems dynamics, we compare the transition from traditional digital propaganda to industrialized “algorithmic campaigns” driven by deepfakes, voice cloning, and media impersonation. Methodologically, we audit a database of documented media impersonations alongside international electoral observation reports. Our comparative analysis suggests that even if synthetic content cannot be shown to directly alter individual votes, it appears to reshape the emotional and symbolic climate of elections, which appears to exacerbate affective polarization and institutional distrust. We contextualize these dynamics within the slop economy—a structural digital divide in information quality between digital elites and digital commoners. Arguing that top-down regulations are insufficient against these decentralized threats, we propose a decentralized democratic defense model based on Jean Cloutier’s émirec concept, introducing cognitive friction into digital consumption to foster bottom-up epistemic resilience.
The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.
Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson· EUROMICRO Conference on Soft...· 64 citations· ⚡6
The goal is to not only refine the accuracy of the LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.
Z. Rasheed, Malik Abdul Sami, Muhammad Waseem et al.· arXiv.org· 62 citations· ⚡3
A comprehensive overview of how enhanced sampling methods are reshaping the field, with a particular focus on the data-driven construction of collective variables, is provided.
Kai Zhu, Enrico Trizio, Jintu Zhang et al.· Chemical Reviews· 58 citations
The use of large language models to automatically improve the user story quality in Austrian Post Group IT agile teams is explored, with a reference model for an Autonomous LLM-based Agent System developed and implemented at the company.
Zheying Zhang, M. Rayhan, Tomas Herda et al.· International Conference on...· 48 citations· ⚡4
This paper introduces a novel multi-AI-agent system designed to fully automate SLRs, and demonstrates how it substantially reduces the time and effort traditionally required for SLRs while maintaining comprehensiveness and precision.
Abdul Malik Sami, Z. Rasheed, Kai-Kristian Kemell et al.· arXiv.org· 44 citations· ⚡2
The proposed LLM-based multi-agent system automates qualitative data analysis process, creating opportunities for researchers and practitioners, and future improvements focus on enhancing multilingual performance and integrating continuous expert feedback.
Z. Rasheed, Muhammad Waseem, Aakash Ahmad et al.· arXiv.org· 41 citations
AI is making software generation faster, but speed does not remove the need for expertise. As more work is delegated to AI, tacit knowledge may become one of the most important human advantages in software engineering. The post Beyond Prompt Engineering: The Role of Tacit Knowledge in Software Engineering appeared first on GPT-Lab.
MIT News · Artificial Intelligence· news.mit.eduSep 16, 2026