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Incidente 836: Sichuan Province Beset by Numerous Fabricated AI-Generated Reports of Disasters and Crises

Descripción: In Sichuan, AI tools were reportedly used to create and disseminate false reports across various cities, including fabricated stories of landslides, earthquakes, standoffs, accidents, and health-related incidents. These AI-driven rumors misled the public, caused alarm and confusion, and strained local authorities. The alleged perpetrators sought social media engagement and financial gain through clickbait content. The authorities in Sichuan ultimately levied administrative penalties against the perpetrators.
Editor Notes: Incident 836 presents an editorial challenge in that it is a composite of 10 discrete AI incidents that point to the possibility of other harm events (see Incidents 834 and 835 as well). "Moumou" and "Mou" refer to partially anonymized names in Chinese media. The following is a reconstruction of each incident outlined in this collective incident report; the majority (but not all) incidents were in Sichuan Province: (1) 11/20/2023 – Fabricated Chengdu Bus Accident: Chen Mou reportedly altered news of a bus crash in Chongqing to claim a similar incident occurred in Chengdu, alleging fatalities and severe impact. (2) 11/28/2023 – False Chengdu Construction Accident Report: Zhu Mou created a fake story about a construction accident in Chengdu’s Longquanyi District, falsely reporting multiple worker fatalities due to an electrical incident. (3) 12/27/2023 – Underground Pipeline Rupture Hoax in Xi’an: Wang Mou and Liu Mou allegedly spread a rumor about a broken underground heating pipeline in Xi’an, using AI to combine information with misleading images to attract attention. (4) 01/12/2024 – Fabricated Abandoned Infant Story in Guiyang: Zhou Mou reportedly used AI to create a story claiming a four-month-old infant was abandoned in Guiyang’s Huaxi District, falsely presenting a distressing social issue. (5) 01/23/2024 – Fabricated Landslide Fatalities in Yunnan: Yang Moumou allegedly spread a fake story claiming that a landslide in Yunnan resulted in eight deaths, using AI-generated information to amplify the incident’s impact (see Incident 835). (6) 04-05/2024 – False Banner and Ceremony Reports in Chengdu: Wang Mou allegedly created fake news about a banner encouraging second births, along with a fictional ceremony in Chengdu’s Pidu District, using AI to create convincing but false visuals. (7) 05/11/2024 – Heart Attack Incident in Xichang: Ji Moumou allegedly created a story about a man who reportedly died of a heart attack following a sex-related incident, spreading the rumor online to gain views. (8) 05/18/2024 – Bazhong Cash Transport Standoff: Shang Moumou allegedly fabricated a report about a standoff involving a cash transport vehicle in Bazhong, claiming a police-citizen confrontation for dramatic effect. (9) 06/09/2024 – Exaggerated Xide County Earthquake Report: Luo Moumou used AI tools to fabricate a story about an earthquake in Xide County, amplifying the damage and claiming casualties, despite the actual quake occurring in Muli County with minimal damage (see Incident 834). (10) 06/24/2024 – Elderly Abandonment Story in Chengdu: Zhang Mou reportedly created a false story of a 90-year-old man abandoned by his children, claiming he was found begging in Chengdu’s Xinjin District, using AI-generated visuals.

Herramientas

Nuevo InformeNuevo InformeNueva RespuestaNueva RespuestaDescubrirDescubrirVer HistorialVer Historial

Entidades

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Alleged: Unknown deepfake technology developers y Unknown AI developers developed an AI system deployed by Zhu Mou , Zhou Moumou , Zhang Moumou , Yang Moumou , Wang Mou , Shang Moumou , Luo Moumou , Liu Mou , Ji Moumou y Chen Mou, which harmed Sichuan general public y Sichuan authorities.

Estadísticas de incidentes

ID
836
Cantidad de informes
1
Fecha del Incidente
2024-07-23
Editores
Daniel Atherton
Applied Taxonomies
MIT

Clasificaciones de la Taxonomía MIT

Machine-Classified
Detalles de la Taxonomía

Risk Subdomain

A further 23 subdomains create an accessible and understandable classification of hazards and harms associated with AI
 

3.1. False or misleading information

Risk Domain

The Domain Taxonomy of AI Risks classifies risks into seven AI risk domains: (1) Discrimination & toxicity, (2) Privacy & security, (3) Misinformation, (4) Malicious actors & misuse, (5) Human-computer interaction, (6) Socioeconomic & environmental harms, and (7) AI system safety, failures & limitations.
 
  1. Misinformation

Entity

Which, if any, entity is presented as the main cause of the risk
 

Human

Timing

The stage in the AI lifecycle at which the risk is presented as occurring
 

Post-deployment

Intent

Whether the risk is presented as occurring as an expected or unexpected outcome from pursuing a goal
 

Intentional

Informes del Incidente

Cronología de Informes

+1
La policía de Internet de Sichuan investigó y resolvió 10 casos típicos de difusión de rumores sobre IA de conformidad con la ley
La policía de Internet de Sichuan investigó y resolvió 10 casos típicos de difusión de rumores sobre IA de conformidad con la ley

La policía de Internet de Sichuan investigó y resolvió 10 casos típicos de difusión de rumores sobre IA de conformidad con la ley

k.sina.cn

La policía de Internet de Sichuan investigó y resolvió 10 casos típicos de difusión de rumores sobre IA de conformidad con la ley
k.sina.cn · 2024
Traducido por IA

Portada Noticias Reportero Gou Chun

En los últimos años, algunos delincuentes han utilizado las funciones de generación de texto e imágenes de la IA para editar información falsa, reduciendo el umbral para que los delincuentes creen y difun…

Variantes

Una "Variante" es un incidente que comparte los mismos factores causales, produce daños similares e involucra los mismos sistemas inteligentes que un incidente de IA conocido. En lugar de indexar las variantes como incidentes completamente separados, enumeramos las variaciones de los incidentes bajo el primer incidente similar enviado a la base de datos. A diferencia de otros tipos de envío a la base de datos de incidentes, no se requiere que las variantes tengan informes como evidencia externa a la base de datos de incidentes. Obtenga más información del trabajo de investigación.

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