Description: Researchers at Stanford Graduate School of Business developed a model that determined, on a binary scale, whether someone was homosexual using only his facial image, which advocacy groups such as GLAAD and the Human Rights Campaign denounced as flawed science and threatening to LGBTQ folks.
Entités
Voir toutes les entitésPrésumé : Un système d'IA développé et mis en œuvre par Michal Kosinski et Yilun Wang, a endommagé LGBTQ people , LGBTQ people of color et non-American LGBTQ people.
Classifications de taxonomie GMF
Détails de la taxonomieKnown AI Goal Snippets
One or more snippets that justify the classification.
(Snippet Text: Presented with photos of gay men and straight men, a computer program was able to determine which of the two was gay with 81 percent accuracy, according to Dr. Kosinski and co-author Yilun Wang’s paper., Related Classifications: Behavioral Modeling, Snippet Discussion: Pairwise classification)
Risk Subdomain
A further 23 subdomains create an accessible and understandable classification of hazards and harms associated with AI
2.1. Compromise of privacy by obtaining, leaking or correctly inferring sensitive 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.
- Privacy & Security
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
Michal Kosinski a estimé qu'il avait de bonnes raisons d'enseigner à une machine à détecter l'orientation sexuelle.
Une start-up israélienne avait commencé à colporter un service qui prédisait les tendances terroristes sur la base d'une ana…
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.
