Description: In a legal case defending Minnesota’s deepfake election misinformation law, Stanford misinformation expert Professor Jeff Hancock's affidavit allegedly cited non-existent academic sources, potentially generated by ChatGPT. The reportedly fabricated citations appear to have undermined the credibility of his testimony.
Editor Notes: Copy of expert declaration: https://storage.courtlistener.com/recap/gov.uscourts.mnd.220348/gov.uscourts.mnd.220348.23.0.pdf (CASE 0:24-cv-03754-LMP-DLM Doc. 23)
Entidades
Ver todas las entidadesAlleged: OpenAI y ChatGPT developed an AI system deployed by Jeff Hancock, which harmed Jeff Hancock , Mary Franson , Keith Ellison , Christopher Kohls y Chad Larson.
Estadísticas de incidentes
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.
- 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
Unintentional
Informes del Incidente
Cronología de Informes
Un destacado experto en desinformación está siendo acusado de citar fuentes inexistentes para defender la nueva ley de Minnesota que prohíbe la desinformación electoral.
El profesor Jeff Hancock, director fundador del Laboratorio de Medios …
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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