Sep 2026· Zenodo (CERN European Organization for Nuclear Research)
Abstract
If someone challenges an AI-made decision three years from now, could your organisation still show how it was made? For most, the honest answer is no. Models are replaced within a year or two, suppliers change hands, reference data is overwritten — while the decision itself lives on: a refused licence follows a person for a career; a land title outlives everyone who touched it. In Europe, the logs of a high-risk AI system may legally be kept for as little as six months. This paper names the missing obligation. Decision custody: an institution's demonstrated ability to reproduce, explain, defend, and answer for every consequential decision made in its name — human or machine — for as long as the consequences last. Grounded in a century of archival science and in ISO 14721, the paper introduces the custody gap, the four tests of custody, and five questions for a board. It is the institutional counterpart to decision sovereignty — and the question the accountability literature has not asked: not whether a decision can be examined, but for how long, and by whom. The covenant is the promise; custody is the proof.
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
MIT professors Adam Berinsky and Chara Podimata discuss a new project to track how AI chatbots are responding to questions about the 2026 U.S. midterm elections.
Microsoft Research Blog· microsoft.comOct 30, 2026
Evaluating Microsoft Copilot at enterprise scale requires test data that captures the full complexity of real organizations, yet real enterprise data contains sensitive information that cannot be used for evaluation under any circumstances. The post From synthetic enterprise data evaluations to real Microsoft Copilot AI confidence appeared first on Microsoft Research.
Critical flaws, compromised release channels and agentic AI threats landed within days of each other in mid-October 2026. Why “Black October” may say more about how well we now find weaknesses than about the attacks themselves. The post Black October? The Week AI Changed the Cybersecurity Conversation appeared first on GPT-Lab.
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