Incidente 103: La herramienta de recorte de imágenes de Twitter supuestamente mostraba sesgo de género y racial
Descripción: Los investigadores revelaron que el algoritmo de recorte de fotografías de Twitter favorece los rostros blancos y de mujeres en fotos que contienen múltiples caras, lo que llevó a la compañía a detener su uso en la plataforma móvil.
Entidades
Ver todas las entidadesPresunto: un sistema de IA desarrollado e implementado por Twitter, perjudicó a Twitter non-white users , Twitter non-male users y X (Twitter) users.
Clasificaciones de la Taxonomía CSETv1
Detalles de la TaxonomíaIncident Number
The number of the incident in the AI Incident Database.
103
Special Interest Intangible Harm
An assessment of whether a special interest intangible harm occurred. This assessment does not consider the context of the intangible harm, if an AI was involved, or if there is characterizable class or subgroup of harmed entities. It is also not assessing if an intangible harm occurred. It is only asking if a special interest intangible harm occurred.
yes
Notes (AI special interest intangible harm)
If for 5.5 you select unclear or leave it blank, please provide a brief description of why.
You can also add notes if you want to provide justification for a level.
The cropping neutral network would crop the preview image in way that focused more on individuals with lighter completions, younger, female, or without disabilities.
Date of Incident Year
The year in which the incident occurred. If there are multiple harms or occurrences of the incident, list the earliest. If a precise date is unavailable, but the available sources provide a basis for estimating the year, estimate. Otherwise, leave blank.
Enter in the format of YYYY
2020
Clasificaciones de la Taxonomía GMF
Detalles de la TaxonomíaKnown AI Goal Snippets
One or more snippets that justify the classification.
(Snippet Text: Twitter‘s algorithm for automatically cropping images attached to tweets often doesn’t focus on the important content in them. , Related Classifications: Image Cropping)
Risk Subdomain
A further 23 subdomains create an accessible and understandable classification of hazards and harms associated with AI
1.1. Unfair discrimination and misrepresentation
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.
- Discrimination and Toxicity
Entity
Which, if any, entity is presented as the main cause of the risk
AI
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