A Comprehensive Study on Evaluating and Mitigating Algorithmic Unfairness with the MADD Metric

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Published Jun 27, 2024
Mélina Verger Chunyang Fan Sébastien Lallé François Bouchet Vanda Luengo

Abstract

Predictive student models are increasingly used in learning environments due to their ability to enhance
educational outcomes and support stakeholders in making informed decisions. However, predictive models
can be biased and produce unfair outcomes, leading to potential discrimination against certain individuals
and harmful long-term implications. This has prompted research on fairness metrics meant to
capture and quantify such biases. Nonetheless, current metrics primarily focus on predictive performance
comparisons between groups, without considering the behavior of the models or the severity of the biases
in the outcomes. To address this gap, we proposed a novel metric in a previous work (Verger et al., 2023)
named Model Absolute Density Distance (MADD), measuring algorithmic unfairness as the difference of
the probability distributions of the model’s outcomes. In this paper, we extended our previous work with
two major additions. Firstly, we provided theoretical and practical considerations on a hyperparameter
of MADD, named bandwidth, useful for optimal measurement of fairness with this metric. Secondly, we
demonstrated how MADD can be used not only to measure unfairness but also to mitigate it through postprocessing
of the model’s outcomes while preserving its accuracy. We experimented with our approach
on the same task of predicting student success in online courses as our previous work, and obtained successful
results. To facilitate replication and future usages of MADD in different contexts, we developed
an open-source Python package called maddlib (https://pypi.org/project/maddlib/). Altogether,
our work contributes to advancing the research on fair student models in education.

How to Cite

Verger, M., Fan, C., Lallé, S., Bouchet, F., & Luengo, V. (2024). A Comprehensive Study on Evaluating and Mitigating Algorithmic Unfairness with the MADD Metric. Journal of Educational Data Mining, 16(1), 365–409. https://doi.org/10.5281/zenodo.12180668
Abstract 42 | HTML Downloads 28 PDF Downloads 25

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Keywords

fairness metric, unfairness mitigation, classification, student modeling, models' behaviors, sensitive features

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