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CSETv1 Taxonomy Classifications
Taxonomy DetailsIncident Number
99
CSETv1_Annotator-1 Taxonomy Classifications
Taxonomy DetailsIncident 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 Taxonomy Classifications
Taxonomy DetailsIncident 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
Incident Reports
Reports Timeline
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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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