How Generative AI Supports Understanding of An Ethically Sensitive AI-Induced Event

Wei, Mengyi; Jiao, Chenjing; Zuo, Chenyu; Hurni, Lorenz; Meng, Liqiu

doi:https://doi.org/10.5194/ica-abs-8-26-2024

Articles | Volume 8

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Articles | Volume 8

https://doi.org/10.5194/ica-abs-8-26-2024

Articles | Volume 8

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11 Nov 2024

| 11 Nov 2024

How Generative AI Supports Understanding of An Ethically Sensitive AI-Induced Event

Mengyi Wei, Chenjing Jiao, Chenyu Zuo, Lorenz Hurni, and Liqiu Meng

Mengyi Wei

Technical University of Munich, Germany

Chenjing Jiao

ETH Zürich, Switzerland

Chenyu Zuo

ETH Zürich, Switzerland

Lorenz Hurni

https://orcid.org/0000-0002-0453-8743

ETH Zürich, Switzerland

Liqiu Meng

Technical University of Munich, Germany

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Short summary

This paper introduces a methodology to support network managers in the quantification of the risk related to their networks. The method emphasizes the integration of the spatial and temporal attributes of the events that need to be modeled to estimate the risk. This work then demonstrates the usefulness of the methodology through its application to design and implement a risk assessment to estimate the potential impact of flood and mudflow events on a road network located in Switzerland.

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Interactive visual exploration and analysis of origin-destination data

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