Fairness
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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Min-Max formulation is a common fairness mechanic utilized in communication system. It works by minimizing the worst case scenario amongst all the nodes in a system, For transmission and energy storage planning applications, we can utilize this fairness mechanic to bolster equity amongst the nodes in our system. For the project this is being utilized for, min-max formulation is used to minimize the worstcase load shedding across each bus in our system. The worst case in this situation being the highest load shedding cost, based on the value of lost load (VoLL) assigned to each bus.
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