Ethics
This dataset contains simulated records for 3,000 students, generated for the purpose of evaluating fairness in predicted grading models. The dataset includes decile rankings based on historical performance, predicted grades, and demographic attributes such as socioeconomic status, school type, gender, and ethnicity. The data was created using controlled randomization techniques and includes noise to reflect real-world prediction uncertainty. While entirely synthetic, the dataset is designed to mimic key structural patterns relevant to algorithmic fairness and educational inequality.
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This dataset contains simulated records for 3,000 students, generated for the purpose of evaluating fairness in predicted grading models. The dataset includes decile rankings based on historical performance, predicted grades, and demographic attributes such as socioeconomic status, school type, gender, and ethnicity. The data was created using controlled randomization techniques and includes noise to reflect real-world prediction uncertainty. While entirely synthetic, the dataset is designed to mimic key structural patterns relevant to algorithmic fairness and educational inequality.
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7 Views
AI Ethics Global Document Collection Daniel Schiff, Jason Borenstein, Justin Biddle, & Kelly Laas Documents in the dataset were published between January 2016 through July 2019 This dataset is associated with a (forthcoming) paper in IEEE Transactions on Technology and Society, entitled "AI Ethics in the Public, Private, and NGO Sectors: A Review of a Global Document Collection.
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883 ViewsAI Ethics Global Document Collection
Daniel Schiff, Jason Borenstein, Justin Biddle, & Kelly Laas
Documents in the dataset were published between January 2016 through July 2019
This dataset is associated with a (forthcoming) paper in IEEE Transactions on Technology and Society, entitled "AI Ethics in the Public, Private, and NGO Sectors: A Review of a Global Document Collection.
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419 Views