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2020

Evaluation of Fairness Trade-offs in Predicting Student Success

Lee, Hansol, Kizilcec, René F.

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Predictive models for identifying at-risk students early can help teaching staff direct resources to better support them, but there is a growing concern about the fairness of algorithmic systems in education.

Predictive models may inadvertently introduce bias in who receives support and thereby exacerbate existing inequities.
We examine this issue by building a predictive model of student success based on university administrative records.
We find that the model exhibits gender and racial bias in two out of three fairness measures considered.

Evaluation of Fairness Trade-offs in Predicting Student Success · Around

Built on

Certifying and removing disparate impact
M. Feldman, S. A. Friedler, J. Moeller, C. Scheidegger, and S. Venkatasubramanian · 2015
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Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
A. Chouldechova · 2016
Earlier work this paper cites.
Equality of opportunity in supervised learning
M. Hardt, E. Price, E. Price, and N. Srebro · 2016
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