Description: In the lead-up to India's 2024 general elections, AI technology was used to create deepfake videos of deceased politicians, such as M. Karunanidhi and J. Jayalalithaa, aiming to influence voter behavior and campaign strategies. These AI-generated appearances are contributing to the erosion of trust in democratic processes and media discourse.
Entités
Voir toutes les entitésAlleged: Muonium et The Indian Deepfaker developed an AI system deployed by Dravida Munnetra Kazhagam , DMK et Various Indian political parties, which harmed Indian electorate , Indian voters , Democracy , Electoral integrity , Media discourse , M. Karunanidhi et J. Jayalalithaa.
Statistiques d'incidents
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
Intentional
Rapports d'incidents
Chronologie du rapport
Bengaluru, India -- On January 23, an icon of Indian cinema and politics, M Karunanidhi appeared before a live audience on a large projected screen, to congratulate his 82-year-old friend and fellow politician TR Baalu on the launch of his …
Bengaluru (India) (AFP) -- Death has not extinguished the decades-long rivalry between two Indian leaders: both have now seemingly risen from the grave, in digital form, to rally their supporters ahead of national elections.
Political parti…
Variantes
Une "Variante" est un incident qui partage les mêmes facteurs de causalité, produit des dommages similaires et implique les mêmes systèmes intelligents qu'un incident d'IA connu. Plutôt que d'indexer les variantes comme des incidents entièrement distincts, nous listons les variations d'incidents sous le premier incident similaire soumis à la base de données. Contrairement aux autres types de soumission à la base de données des incidents, les variantes ne sont pas tenues d'avoir des rapports en preuve externes à la base de données des incidents. En savoir plus sur le document de recherche.