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

2026-06-06

AI Summary

This study addresses the critical gap in AI safety governance caused by the absence of large-scale, structured data on real-world risk incidents. To bridge this gap, the authors construct the first large-scale dataset of AI risk events, derived from hundreds of millions of multilingual news articles. Through systematic event identification, cross-report alignment, multidimensional risk categorization, and structured organization, the work produces standardized, event-centric records. The contributions include: the first large-scale, aligned dataset of AI risk event clusters; a benchmark subset annotated across multiple risk dimensions; and an online platform enabling interactive exploration and analysis. Together, these resources provide essential infrastructure for empirical AI safety research and help translate high-level governance principles into evidence-based practice.

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. Although existing governance frameworks articulate high-level principles for responsible AI, large-scale empirical resources for tracking and analyzing real-world AI risk incidents remain limited. Existing incident collections are often manually curated, relatively small in scale, and insufficient for continuous, data-driven monitoring and downstream computational analysis. To address this need, we present RiskNet, a large-scale dataset of AI risk incidents constructed from large-scale multilingual news sources. RiskNet applies a structured pipeline for AI risk news identification, event-level report screening, incident alignment, and multi-dimensional incident classification. The resulting resource organizes dispersed news reports into incident-centered records and provides benchmark datasets for event classification, incident alignment, and incident-level risk labeling. In its current release, RiskNet covers hundreds of millions of source records and yields a large-scale collection of AI risk-related reports, including aligned incident clusters and annotated benchmark subsets. The dataset is also accessible through an online platform for browsing and exploration. We describe the data sources, processing workflow, taxonomy design, and technical validation of the resource. RiskNet is intended to support downstream research on AI safety, governance, risk analysis, and benchmarking, as well as longitudinal and cross-source analyses of AI-related harms. By providing a structured and reusable empirical resource, RiskNet helps bridge the gap between high-level governance principles and the documented realities of AI risk incidents.

Problem

• AI risk incidents
• large-scale dataset
• empirical resource
• incident monitoring
• computational analysis

Innovation

• AI risk incidents
• incident alignment
• multi-dimensional annotation
• large-scale dataset
• structured pipeline