Sep 2026· Socialʹnye i gumanitarnye znania· 18 references
Abstract
The article examines the media image of artificial intelligence as a significant element of the mass political imagination in the digital age. Its aim is to provide a political-science interpretation of the discursive frames through which AI becomes an object of public legitimation or criticism in the context of transformations in public administration, sovereignty, and civic autonomy. Methodologically, the study relies on political discourse analysis, frame analysis, and a conceptual interpretation of contemporary scholarship on algorithmic governance, public legitimacy, datafication, mediatization, and sociotechnical imaginaries. The article argues that, in contemporary public discourse, AI is primarily framed through two competing images: the "smart state" and the "digital Leviathan". The former associates AI with service efficiency, convenience, and managerial rationalization, whereas the latter foregrounds the opacity of digital power, asymmetries of data, hidden selection, and deficits of accountability. It is shown that the political significance of AI is determined less by its technical characteristics than by the normative regimes through which it is publicly interpreted. The article concludes that the legitimacy of digital governance depends on institutional limits placed on algorithmic power, clearly identifiable responsibility, procedural fairness, explainability, and the right to contest automated decisions.
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
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
The “HardFlow” algorithm could help generative AI models produce high-quality outputs that obey strict requirements when “pretty close” doesn’t cut it.
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