概要: An automated license plate reader (ALPR) camera misread a 7 as a 2 and incorrectly alerted the local police about a stolen Oldsmobile car, which was allegedly not able to be verified by an officer before a traffic stop was effected on a BMW in Kansas City suburb.
Alleged: unknown developed an AI system deployed by Prairie Village Police Department, which harmed Mark Molner.
Risk Subdomain
A further 23 subdomains create an accessible and understandable classification of hazards and harms associated with AI
7.3. Lack of capability or robustness
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.
- AI system safety, failures, and limitations
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
With license plate reader (LPR) use rapidly expanding throughout the United States, it's no surprise that sometimes officers pull over motorists—at gunpoint—for mistakes made by the automated camera system.
The latest incident happened near…
Automatic license plate readers can scan plates at a rate of one per second. Nationwide, several hundred million plate/location records have been captured and stored by a variety of contractors. Mathematics alone says mistakes will be made.…
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