インシデントのステータス
CSETv1 分類法のクラス
分類法の詳細Incident Number
99
CSETv1_Annotator-1 分類法のクラス
分類法の詳細Incident Number
99
AI Tangible Harm Level Notes
3.2 - The algorithms were trained on historic data and analyzed student data to predict their risk of dropping out.
CSETv1_Annotator-2 分類法のクラス
分類法の詳細Incident Number
99
Notes (special interest intangible harm)
Race and income level are sometimes used to predict how likely a university student will drop out of school.
Special Interest Intangible Harm
yes
Notes (AI special interest intangible harm)
It is unclear if the algorithm is AI or developed by some other means, like a rule-based decision-making algorithm.
Date of Incident Year
2021
Date of Incident Month
03
インシデントレポート
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Major universities are using their students’ race, among other variables, to predict how likely they are to drop out of school. Documents obtained by The Markup through public records requests show that some schools are using education rese…
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