概要: インフィニット・キャンパスが開発し、ネバダ州がリスクのある生徒を特定するために導入したAIシステムにより、支援が必要と分類された生徒数は27万人から6万5千人に激減しました。この再分類により、低所得者層の学校は大幅な予算削減に直面しました。特定されたリスクのある生徒の大幅な減少により、数千人もの脆弱な立場にある子どもたちがリソースと支援を得られなくなったと報告されています。
Editor Notes: Timeline notes and clarification: Before 2023, Nevada identified at-risk students mostly by income, using free or reduced-price lunch eligibility as the key measure. In 2022, this system classified over 270,000 students as at-risk. Looking to improve the process, Nevada partnered with Infinite Campus in 2023 to introduce an AI system that used more factors like GPA, attendance, household structure, and home language. The new system was meant to better predict which students might struggle in school. However, during the 2023-2024 school year, the AI cut the number of at-risk students to less than 65,000. This reclassification caused budget cuts in schools that depended on the funding tied to at-risk students, especially those serving low-income populations. By October 2024, the problem gained national attention.
Alleged: Infinite Campus developed an AI system deployed by Nevada Department of Education, which harmed Low-income students in Nevada , Nevada school districts , Mater Academy of Nevada と Somerset Academy.
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
1.3. 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