We examine how expertise, evidence and authority are organised in the international governance of Artificial Intelligence (AI), and what this reveals about the practice of science diplomacy. Science-diplomacy scholarship has become increasingly attentive to the political character of expertise and to the ways knowledge is mobilised within international governance. Yet less attention has been paid to the organisational practices through which such knowledge is brokered within governance processes. Drawing on documentary analysis and 23 semi-structured interviews, we compare UNESCO’s Recommendation on the Ethics of Artificial Intelligence and the EU AI Act. Through an abductive analysis, we develop Organisational Knowledge Brokerage (OKB) to explain how a contested object such as AI is made workable for governance. We identify four connected practices through which this occurs: stabilising the governance object, legitimising expertise, building authority and operationalising governance. These practices took different forms under contrasting institutional mandates and governing capacities. UNESCO worked through global intergovernmental standard-setting and implementation support, while the EU combined supranational regulation with expert advice and Member State coordination. Across both cases, OKB also produced a cross-cutting process that we term normative translation, through which contested values acquired practical meaning in definitions, assessments and procedures. We show how this brokerage allowed each organisation to establish workable and revisable settlements around AI under conditions of technological uncertainty and dispersed authority. The article therefore develops a practice-oriented account of science diplomacy as organisational work through which knowledge, values and institutional authority are assembled for collective action in international technology governance.
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
A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.