Skip to Content
logologo
AI Incident Database
Open TwitterOpen RSS FeedOpen FacebookOpen LinkedInOpen GitHub
Open Menu
発見する
投稿する
  • ようこそAIIDへ
  • インシデントを発見
  • 空間ビュー
  • テーブル表示
  • リスト表示
  • 組織
  • 分類法
  • インシデントレポートを投稿
  • 投稿ランキング
  • ブログ
  • AIニュースダイジェスト
  • リスクチェックリスト
  • おまかせ表示
  • サインアップ
閉じる
発見する
投稿する
  • ようこそAIIDへ
  • インシデントを発見
  • 空間ビュー
  • テーブル表示
  • リスト表示
  • 組織
  • 分類法
  • インシデントレポートを投稿
  • 投稿ランキング
  • ブログ
  • AIニュースダイジェスト
  • リスクチェックリスト
  • おまかせ表示
  • サインアップ
閉じる

インシデント 96: Houston Schools Must Face Teacher Evaluation Lawsuit

概要: On May 4, 2017, a U.S. federal judge advanced teachers’ claims that the Houston Independent School District’s algorithmic teacher evaluations violated their due process rights to their jobs by not allowing them to review the grounds of their termination.

ツール

新しいレポート新しいレポート新しいレスポンス新しいレスポンス発見する発見する履歴を表示履歴を表示

組織

すべての組織を表示
Alleged: SAS Institute developed an AI system deployed by Houston Independent School District, which harmed Houston Independent School District teachers.

インシデントのステータス

インシデントID
96
レポート数
1
インシデント発生日
2017-05-08
エディタ
Sean McGregor, Khoa Lam
Applied Taxonomies
CSETv0, CSETv1, GMF, MIT

CSETv0 分類法のクラス

分類法の詳細

Problem Nature

Indicates which, if any, of the following types of AI failure describe the incident: "Specification," i.e. the system's behavior did not align with the true intentions of its designer, operator, etc; "Robustness," i.e. the system operated unsafely because of features or changes in its environment, or in the inputs the system received; "Assurance," i.e. the system could not be adequately monitored or controlled during operation.
 

Specification

Physical System

Where relevant, indicates whether the AI system(s) was embedded into or tightly associated with specific types of hardware.
 

Software only

Level of Autonomy

The degree to which the AI system(s) functions independently from human intervention. "High" means there is no human involved in the system action execution; "Medium" means the system generates a decision and a human oversees the resulting action; "low" means the system generates decision-support output and a human makes a decision and executes an action.
 

Low

Nature of End User

"Expert" if users with special training or technical expertise were the ones meant to benefit from the AI system(s)’ operation; "Amateur" if the AI systems were primarily meant to benefit the general public or untrained users.
 

Amateur

Public Sector Deployment

"Yes" if the AI system(s) involved in the accident were being used by the public sector or for the administration of public goods (for example, public transportation). "No" if the system(s) were being used in the private sector or for commercial purposes (for example, a ride-sharing company), on the other.
 

No

Data Inputs

A brief description of the data that the AI system(s) used or were trained on.
 

test grades

CSETv1 分類法のクラス

分類法の詳細

Incident Number

The number of the incident in the AI Incident Database.
 

96

AI Tangible Harm Level Notes

Notes about the AI tangible harm level assessment
 

3.5 - the value-added measurement/modeling is not AI - it is a statistical model

MIT 分類法のクラス

Machine-Classified
分類法の詳細

Risk Subdomain

A further 23 subdomains create an accessible and understandable classification of hazards and harms associated with AI
 

7.4. Lack of transparency or interpretability

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.
 
  1. 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

+1
Houston Schools Must Face Teacher Evaluation Lawsuit
Houston Schools Must Face Teacher Evaluation Lawsuit

Houston Schools Must Face Teacher Evaluation Lawsuit

courthousenews.com

Houston Schools Must Face Teacher Evaluation Lawsuit
courthousenews.com · 2017

HOUSTON (CN) – A proprietary system that measures teacher performance based on student test scores may violate teachers’ civil rights because they can’t verify the results are accurate, a federal judge ruled, advancing a lawsuit against Tex…

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

テキスト類似度による

Did our AI mess up? Flag the unrelated incidents

Predictive Policing Program by Florida Sheriff’s Office Allegedly Violated Residents’ Rights and Targeted Children of Vulnerable Groups

Predictive policing strategies for children face pushback

Sep 2015 · 12 レポート
NY City School Teacher Evaluation Algorithm Contested

Analyzing Released NYC Value-Added Data Part 2

Feb 2012 · 7 レポート
Northpointe Risk Models

Machine Bias - ProPublica

May 2016 · 15 レポート
前のインシデント次のインシデント

よく似たインシデント

テキスト類似度による

Did our AI mess up? Flag the unrelated incidents

Predictive Policing Program by Florida Sheriff’s Office Allegedly Violated Residents’ Rights and Targeted Children of Vulnerable Groups

Predictive policing strategies for children face pushback

Sep 2015 · 12 レポート
NY City School Teacher Evaluation Algorithm Contested

Analyzing Released NYC Value-Added Data Part 2

Feb 2012 · 7 レポート
Northpointe Risk Models

Machine Bias - ProPublica

May 2016 · 15 レポート

リサーチ

  • “AIインシデント”の定義
  • “AIインシデントレスポンス”の定義
  • データベースのロードマップ
  • 関連研究
  • 全データベースのダウンロード

プロジェクトとコミュニティ

  • AIIDについて
  • コンタクトとフォロー
  • アプリと要約
  • エディタのためのガイド

インシデント

  • 全インシデントの一覧
  • フラグの立ったインシデント
  • 登録待ち一覧
  • クラスごとの表示
  • 分類法

2024 - AI Incident Database

  • 利用規約
  • プライバシーポリシー
  • Open twitterOpen githubOpen rssOpen facebookOpen linkedin
  • ecd56df