Descripción: In October 2021, a coordinated network of over 317 fake Twitter accounts leveraged AI-driven algorithms to amplify disinformation about the Honduran presidential election, targeting opposition candidate Xiomara Castro. The campaign spread false narratives to suppress voter turnout and undermine the election's integrity. Social media platforms, including Twitter and Facebook, removed the accounts only after being alerted, which also raised concerns about inadequate moderation.
Editor Notes: Reconstructing the timeline of events: (1) October 7, 2021: A coordinated network of 19 Twitter accounts posts identical disinformation about opposition candidate Xiomara Castro, falsely suggesting an alliance with Yani Rosenthal. The accounts use profile photos linked to uninvolved Peruvians. (2) October 6–14, 2021: Over 317 fake Twitter accounts amplify disinformation, creating feedback loops with a fake news website designed to resemble a legitimate outlet, spreading false claims about Castro and discouraging voter participation. (3) Early November 2021: Cybersecurity firm Nisos identifies the coordinated disinformation campaign and reports it to Twitter. (4) Early November 2021: Twitter removes the fake accounts after receiving the analysis from Nisos. (5) November 15, 2021: TIME publishes an article detailing the disinformation campaign and the role of AI-driven social media algorithms in amplifying the false narratives.
Entidades
Ver todas las entidadesAlleged: X (Twitter) , Meta y Facebook developed an AI system deployed by National Party of Honduras supporters , Juan Orlando Hernández supporters , Unknown Twitter users y Unknown Facebook users, which harmed Xiomara Castro , Libertad y Refundación (LIBRE) supporters , Honduran electorate , Honduras , Democracy y Electoral integrity.
Estadísticas de incidentes
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
El 7 de octubre a las 22:16 horas, un grupo de diecinueve cuentas de Twitter compartieron opiniones idénticas sobre las próximas elecciones presidenciales en Honduras en el mismo momento. Afirmando ser partidarios de la candidata de la opos…
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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