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Related Experiment Video

Updated: Sep 15, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

A unified ontological and explainable framework for decoding AI risks from news data.

Chuan Chen¹, Peng Luo^2,3, Huilin Zhao⁴

¹Chair of Cartography and Visual Analytics, Technical University of Munich, Munich, Germany.

Scientific Reports

|July 15, 2025

Summary

This summary is machine-generated.

This study introduces a unified ontological model to comprehensively map artificial intelligence (AI) risks, from broad categories to specific events. It uses visual analytics and machine learning to analyze AI risk data, providing new insights into driving factors.

Keywords:

AI ethics AI risk Explainable machine learning News data Ontological model

Related Experiment Videos

Last Updated: Sep 15, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Area of Science:

Computer Science
Artificial Intelligence
Data Science
Risk Management

Background:

Artificial intelligence (AI) is increasingly integrated into daily life, leading to significant concerns about its potential risks.
Current research on AI risks is fragmented, lacking a unified framework to connect high-level risk typologies with specific real-world incidents.
A comprehensive approach is needed to systematically understand and analyze the multifaceted nature of AI-related risks.

Purpose of the Study:

To develop a novel ontological risk model for a unified representation of AI risks across multiple scales.
To construct an enriched AI risk event database from raw news data.
To identify key characteristics and driving factors of AI risk events using advanced analytical techniques.

Main Methods:

Development of an ontological risk model to structure AI risk information.
Systematic extraction and structuring of raw news data into an enriched AI risk event database.
Application of visual analytics for summarizing AI risk event characteristics.
Integration of explainable machine learning (ML) to identify risk drivers.

Main Results:

A comprehensive framework for representing AI risks, bridging macro-level typologies and micro-level instances.
An enriched database of AI risk events derived from news data.
Identification of key patterns and potential causal factors associated with different AI risk attributes through visual and ML analysis.

Conclusions:

The proposed ontological model offers a novel, quantitative approach to understanding AI risks.
The study provides both structural insights via ontological modeling and mechanistic interpretations via explainable ML.
This framework facilitates a more holistic and actionable understanding of AI risks for researchers and policymakers.