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Assessing Disparities in Predictive Modeling Outcomes for College Student Success: The Impact of Imputation Techniques on Model Performance and Fairness
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Nazanin Nezami; Parian Haghighat; Denisa Gándara; Hadis Anahideh – Grantee Submission, 2024
The education sector has been quick to recognize the power of predictive analytics to enhance student success rates. However, there are challenges to widespread adoption, including the lack of accessibility and the potential perpetuation of inequalities. These challenges present in different stages of modeling, including data preparation, model…
Descriptors: Evaluation Methods, College Students, Success, Predictor Variables
Inside the Black Box: Detecting and Mitigating Algorithmic Bias across Racialized Groups in College Student-Success Prediction
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Denisa Gándara; Hadis Anahideh; Matthew P. Ison; Lorenzo Picchiarini – AERA Open, 2024
Colleges and universities are increasingly turning to algorithms that predict college-student success to inform various decisions, including those related to admissions, budgeting, and student-success interventions. Because predictive algorithms rely on historical data, they capture societal injustices, including racism. In this study, we examine…
Descriptors: Algorithms, Social Bias, Minority Groups, Equal Education
Inside the Black Box: Detecting and Mitigating Algorithmic Bias across Racialized Groups in College Student-Success Prediction
Peer reviewed Peer reviewed
Direct linkDirect link
Denisa Gándara; Hadis Anahideh; Matthew P. Ison; Lorenzo Picchiarini – Grantee Submission, 2024
Colleges and universities are increasingly turning to algorithms that predict college-student success to inform various decisions, including those related to admissions, budgeting, and student-success interventions. Because predictive algorithms rely on historical data, they capture societal injustices, including racism. In this study, we examine…
Descriptors: Algorithms, Social Bias, Minority Groups, Equal Education
Finding Representative Group Fairness Metrics Using Correlation Estimations
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Hadis Anahideh; Nazanin Nezami; Abolfazl Asudeh – Grantee Submission, 2025
It is of critical importance to be aware of the historical discrimination embedded in the data and to consider a fairness measure to reduce bias throughout the predictive modeling pipeline. Given various notions of fairness defined in the literature, investigating the correlation and interaction among metrics is vital for addressing unfairness.…
Descriptors: Correlation, Measurement Techniques, Guidelines, Semantics