arXiv AI By Leihan Zhang, Wecheng Ye, Xianlong Ma, Haochuan Liu, Yang Li, Qianyu Zhang, Jinliang Chen, Qiang Yan

RiskNet: A large-scale dataset of AI risk incidents from news with alignment and multi-dimensional annotations

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arXiv:2606. 08376v1 Announce Type: cross Abstract: As artificial intelligence (AI) systems are increasingly deployed across socially consequential domains, reports of AI-related harms and failures have grown in frequency and diversity.

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arXiv Computation and Language
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DisCTI: Who Needs to Know Timely? Automated Sector-Aware Cyber Threat Intelligence Dissemination

The paper introduces DisCTI, a system that automatically maps cyber threat intelligence (CTI) events to relevant industry sectors using a multilabel classification approach. By creating a dataset of 872 sector‑labelled CTI events and applying a BERT transformer model, the authors achieve a macro‑averaged F1‑score of 0.89, correctly assigning 94.5% of sector labels. This demonstrates that embedding expert knowledge into machine learning can enable timely, sector‑aware CTI dissemination, improving defensive response.

By Fajar Wijitrisnanto (National Cyber and Crypto Agency, Jakarta, Indonesia), Alsharif Abuadbba (CSIRO, Sydney, Australia), Yansong Gao (CSIRO, Sydney, Australia, The University of Western Australia, Perth, Australia), Nan Wu (CSIRO, Sydney, Australia)
arXiv AI
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Open Problems in AI Incident Governance

arXiv:2607. 05163v1 Announce Type: cross Abstract: AI systems may produce failures after deployment that pre-deployment safety assessments do not anticipate.

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