概要: Sometime in February 2024, Shaun Thompson is reported to have walked by one of the London Metropolitan Police's facial recognition technology vans near London Bridge. He was almost immediately arrested because the technology is reported to have misidentified him as a suspect in an unrelated and unspecified crime.
Editor Notes: Incidents 691 and 692 are paired together in the reporting, but they are two separate, discrete harm events. I have created distinct incident IDs for each while replicating the reporting for each one.
The incident date of 2/1/2024 gestures toward the fact that we seem only to have "sometime in February" as the date of Shaun Thompson's misidentification and arrest.
インシデントのステータス
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
インシデントレポート
レポートタイムライン
Sara needed some chocolate - she had had one of those days - so wandered into a Home Bargains store.
"Within less than a minute, I'm approached by a store worker who comes up to me and says, 'You're a thief, you need to leave the store'."
S…
Shoppers have been warned that facial recognition software used by stores is wrongly identifying some innocent customers as thieves.
A woman said that an employee at Home Bargains, the variety store chain, accused her of being a shoplifter …
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
