Description: Facebook’s hate-speech detection algorithms was found by company researchers to have under-reported less common but more harmful content that was more often experienced by minority groups such as Black, Muslim, LGBTQ, and Jewish users.
Alleged: Facebook developed and deployed an AI system, which harmed Facebook users of minority groups と Facebook users.
Risk Subdomain
A further 23 subdomains create an accessible and understandable classification of hazards and harms associated with AI
1.2. Exposure to toxic content
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
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
インシデントレポート
レポートタイムライン
In public, Facebook seems to claim that it removes more than 90 percent of hate speech on its platform, but in private internal communications the company says the figure is only an atrocious 3 to 5 percent. Facebook wants us to believe tha…
Last year, researchers at Facebook showed executives an example of the kind of hate speech circulating on the social network: an actual post featuring an image of four female Democratic lawmakers known collectively as “The Squad.”
The poste…
バリアント
「バリアント」は既存のAIインシデントと同じ原因要素を共有し、同様な被害を引き起こし、同じ知的システムを含んだインシデントです。バリアントは完全に独立したインシデントとしてインデックスするのではなく、データベースに最初に投稿された同様なインシデントの元にインシデントのバリエーションとして一覧します。インシデントデータベースの他の投稿タイプとは違い、バリアントではインシデントデータベース以外の根拠のレポートは要求されません。詳細についてはこの研究論文を参照してください
よく似たインシデント
Did our AI mess up? Flag the unrelated incidents
よく似たインシデント
Did our AI mess up? Flag the unrelated incidents