Sep 2026· Zenodo (CERN European Organization for Nuclear Research)· 2 citations
Ethics and Social Impacts of AI
Abstract
European artificial intelligence governance is administered largely through words. Obligations attach to defined roles, evidence is assembled in documents, and claims travel through contracts and procurement files long after the conversation that produced them has ended. This research note examines a practical problem: terms that appear synonymous in ordinary professional usage — user and deployer, provider and vendor, training and AI literacy, certificate and certification, compliance and conformity — carry different legal, contractual and evidentiary weight. The method is documentary: each definition and provision is traced to a primary source, and every material claim is classified as law, official guidance, standard, accreditation practice, author analysis or author's proposed model. Two symmetrical findings organise the argument. A single substantive concept can be relabelled during the legislative process, as the 2021 Commission proposal's "user" became the adopted Regulation's "deployer". Conversely, an obligation can be materially rewritten while keeping its name and its place in the text, as Article 4 of the AI Act was by Regulation (EU) 2026/1744. A third finding runs through both: the evidentiary value of a document depends on the regime that issued it, not on the word printed on its face. The note proposes a TERM–CLAIM–EVIDENCE model — an author's operational instrument, not a legal requirement — for organisations whose vocabulary has to survive a contract, a tender file or an audit.
GAOKAO-Bench is introduced, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples, including both subjective and objective questions that contribute a robust evaluation benchmark for future large language models and offers valuable insights into the advantages and limitations of such models.
Xiaotian Zhang, Chun-yan Li, Yi Zong et al.· arXiv.org· 216 citations· ⚡17
Empirically, PRISM reduces the end-to-end time for data selection and model tuning to just 30% of conventional pipelines, and achieves this efficiency while simultaneously enhancing performance, surpassing models fine-tuned on the full dataset across eight multimodal and three language understanding benchmarks.
Jinhe Bi, Yifan Wang, Danqi Yan et al.· arXiv.org· 73 citations· ⚡4
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
This paper designs Markov decision processes (MDPs) for different combinatorial problems and proposes to train conditional GFlowNets to sample from the solution space and demonstrates that GFlowNet policies can efficiently find high-quality solutions.
Dinghuai Zhang, H. Dai, Esmeralda S. Whitammer et al.· Advances in Neural Informati...· 59 citations· ⚡8
An empirical study on the current state of practice in artificial intelligence ethics is conducted by means of a multiple case study of five case companies, which indicates a gap between research and practice in the area.
Ville Vakkuri, Kai-Kristian Kemell, Joni Kultanen et al.· arXiv.org· 56 citations· ⚡6