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インシデント 78: Meet the Secret Algorithm That's Keeping Students Out of College

概要: In response to the Covid-19 pandemic, the International Baccalaureate final exams were replaced by a calculated score, prompting complaints of unfairness from teachers and students.

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組織

すべての組織を表示
推定: International Baccalauretteが開発し提供したAIシステムで、International Baccalaureate studentsに影響を与えた

インシデントのステータス

インシデントID
78
レポート数
1
インシデント発生日
2020-07-06
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.
 

Unknown/unclear

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.
 

Expert

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.
 

Prior test and exam grades, school attended

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.
 

78

AI Tangible Harm Level Notes

Notes about the AI tangible harm level assessment
 

The harm was caused by a statistical algorithm that did not meet our definition of AI. Harm did occur, but it was intangible (opportunity loss) instead of tangible.

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
 

7.3. 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.
 
  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

Incident OccurrenceMeet the Secret Algorithm That's Keeping Students Out of College
Meet the Secret Algorithm That's Keeping Students Out of College

Meet the Secret Algorithm That's Keeping Students Out of College

wired.com

Meet the Secret Algorithm That's Keeping Students Out of College
wired.com · 2020

EIGHTEEN-YEAR-OLD ANAHITA NAGPAL fears her plans to start training this fall to be a doctor have been derailed by a statistical model.

Nagpal, who lives in Göttingen, Germany, had been offered a premed place and scholarship at NYU. Her acce…

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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テキスト類似度による

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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

Racist AI behaviour is not a new problem

Racist AI behaviour is not a new problem

Mar 1998 · 4 レポート
NY City School Teacher Evaluation Algorithm Contested

Analyzing Released NYC Value-Added Data Part 2

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