インシデント 168: translated-ja-Collaborative Filtering Prone to Popularity Bias, Resulting in Overrepresentation of Popular Items in the Recommendation Outputs
概要: translated-ja-Collaborative filtering prone to popularity bias, resulting in overrepresentation of popular items in the recommendation outputs.
推定: Facebook , LinkedIn , YouTube , Twitter と Netflixが開発し提供したAIシステムで、Facebook users , LinkedIn users , YouTube users , Twitter Users と Netflix usersに影響を与えた
Risk Subdomain
A further 23 subdomains create an accessible and understandable classification of hazards and harms associated with AI
1.3. Unequal performance across groups
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
インシデントレポート
レポートタイムライン
Introduction
Collaborative filtering (CF) is one of the most traditional but also most powerful concepts for calculating personalized recommendations [22] and is vastly used in the field of multimedia recommender systems (MMRS) [11]. Howeve…
If you’re interested in obscure things, there are two reasons why your searches for items and products are likely to be less related to your interests than those of your ‘mainstream’ peers; either you’re a monetization ‘edge case’ whose int…
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
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