Inside the Black Box: Detecting and Mitigating Algorithmic Bias Across Racialized Groups in College Student-Success Prediction
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 injusti...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2024-06-01
|
| Series: | AERA Open |
| Online Access: | https://doi.org/10.1177/23328584241258741 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832584924189687808 |
|---|---|
| author | Denisa Gándara Hadis Anahideh Matthew P. Ison Lorenzo Picchiarini |
| author_facet | Denisa Gándara Hadis Anahideh Matthew P. Ison Lorenzo Picchiarini |
| author_sort | Denisa Gándara |
| collection | DOAJ |
| description | 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 how the accuracy of college student success predictions differs between racialized groups, signaling algorithmic bias. We also evaluate the utility of leading bias-mitigating techniques in addressing this bias. Using nationally representative data from the Education Longitudinal Study of 2002 and various machine learning modeling approaches, we demonstrate how models incorporating commonly used features to predict college-student success are less accurate when predicting success for racially minoritized students. Common approaches to mitigating algorithmic bias are generally ineffective at eliminating disparities in prediction outcomes and accuracy between racialized groups. |
| format | Article |
| id | doaj-art-497a211e6ed64c0e922aac3776b0ee7a |
| institution | Kabale University |
| issn | 2332-8584 |
| language | English |
| publishDate | 2024-06-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | AERA Open |
| spelling | doaj-art-497a211e6ed64c0e922aac3776b0ee7a2025-01-27T09:03:23ZengSAGE PublishingAERA Open2332-85842024-06-011010.1177/23328584241258741Inside the Black Box: Detecting and Mitigating Algorithmic Bias Across Racialized Groups in College Student-Success PredictionDenisa GándaraHadis AnahidehMatthew P. IsonLorenzo PicchiariniColleges 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 how the accuracy of college student success predictions differs between racialized groups, signaling algorithmic bias. We also evaluate the utility of leading bias-mitigating techniques in addressing this bias. Using nationally representative data from the Education Longitudinal Study of 2002 and various machine learning modeling approaches, we demonstrate how models incorporating commonly used features to predict college-student success are less accurate when predicting success for racially minoritized students. Common approaches to mitigating algorithmic bias are generally ineffective at eliminating disparities in prediction outcomes and accuracy between racialized groups.https://doi.org/10.1177/23328584241258741 |
| spellingShingle | Denisa Gándara Hadis Anahideh Matthew P. Ison Lorenzo Picchiarini Inside the Black Box: Detecting and Mitigating Algorithmic Bias Across Racialized Groups in College Student-Success Prediction AERA Open |
| title | Inside the Black Box: Detecting and Mitigating Algorithmic Bias Across Racialized Groups in College Student-Success Prediction |
| title_full | Inside the Black Box: Detecting and Mitigating Algorithmic Bias Across Racialized Groups in College Student-Success Prediction |
| title_fullStr | Inside the Black Box: Detecting and Mitigating Algorithmic Bias Across Racialized Groups in College Student-Success Prediction |
| title_full_unstemmed | Inside the Black Box: Detecting and Mitigating Algorithmic Bias Across Racialized Groups in College Student-Success Prediction |
| title_short | Inside the Black Box: Detecting and Mitigating Algorithmic Bias Across Racialized Groups in College Student-Success Prediction |
| title_sort | inside the black box detecting and mitigating algorithmic bias across racialized groups in college student success prediction |
| url | https://doi.org/10.1177/23328584241258741 |
| work_keys_str_mv | AT denisagandara insidetheblackboxdetectingandmitigatingalgorithmicbiasacrossracializedgroupsincollegestudentsuccessprediction AT hadisanahideh insidetheblackboxdetectingandmitigatingalgorithmicbiasacrossracializedgroupsincollegestudentsuccessprediction AT matthewpison insidetheblackboxdetectingandmitigatingalgorithmicbiasacrossracializedgroupsincollegestudentsuccessprediction AT lorenzopicchiarini insidetheblackboxdetectingandmitigatingalgorithmicbiasacrossracializedgroupsincollegestudentsuccessprediction |