Descripción: A purported deepfake audio clip, falsely attributed to Imran Khan urging a PTI (Pakistan Tehreek-e-Insaf) election boycott, circulated on social media on the eve of Pakistan's general elections. This sophisticated AI-generated misinformation aimed to mislead voters, highlighting the growing challenge of digital manipulation in political discourse.
Entidades
Ver todas las entidadesAlleged: unknown developed an AI system deployed by Unknown social media accounts, which harmed Voters in Pakistan , PTI (Pakistan Tehreek-e-Insaf) , Imran Khan y Democracy.
Risk Subdomain
A further 23 subdomains create an accessible and understandable classification of hazards and harms associated with AI
4.1. Disinformation, surveillance, and influence at scale
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.
- Malicious Actors & Misuse
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
On February 7, 2024, just a day before Pakistan's highly anticipated general elections, from which former Prime Minister Imran Khan was barred because of a graft conviction, a voice recording alleged to be of the imprisoned popular leader c…
In the run-up to Pakistan's general elections this month, the country's 128 million voters were exposed to disinformation, including through artificial intelligence and deepfake videos.
Aside from a much talked about AI video of former Prim…
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.