インシデント 84の引用情報
インシデントのステータス
CSETv0 分類法のクラス
分類法の詳細Full Description
Avaaz, an international advocacy group, released a review of Facebook's misinformation identifying software showing that the labeling process failed to label 42% of false information posts, most surrounding COVID-19 and the 2020 USA Presidential Election. Avaaz found that by adjusting the cropping or background of a post containing misinformation, the Facebook algorithm would fail to recognize it as misinformation, allowing it to be posted and shared without a cautionary label.
Short Description
Avaaz, an international advocacy group, released a review of Facebook's misinformation identifying software showing that the labeling process failed to label 42% of false information posts, most surrounding COVID-19 and the 2020 USA Presidential Election.
Severity
Unclear/unknown
Harm Type
Harm to social or political systems
AI System Description
Facebook's algorithm and process used to place cautionary labels on posts that are decided to contain misinformation
System Developer
Sector of Deployment
Information and communication
Relevant AI functions
Perception, Cognition
AI Techniques
Language recognition, content filtering, image recognition
AI Applications
misinformation labeling, image recognition, image labeling
Location
Global
Named Entities
Facebook, Avaaz, Reuters, AP, PolitiFact
Technology Purveyor
Beginning Date
2020-10-09T07:00:00.000Z
Ending Date
2020-10-09T07:00:00.000Z
Near Miss
Unclear/unknown
Intent
Unclear
Lives Lost
No
Infrastructure Sectors
Communications
Data Inputs
User posts
CSETv1 分類法のクラス
分類法の詳細Harm Distribution Basis
none
Sector of Deployment
information and communication
インシデントレポート
レポートタイムライン
- 情報源として元のレポートを表示
- インターネットアーカイブでレポートを表示
Something as simple as changing the font of a message or cropping an image can be all it takes to bypass Facebook's defenses against hoaxes and lies.
A new analysis by the international advocacy group Avaaz shines light on why, despite the …
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