Is Your Model "MADD"? A Novel Metric to Evaluate Algorithmic Fairness for Predictive Student Models

Understand

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 some students and possible harmful long-term implications.
• This has prompted research on fairness metrics meant to capture and quantify such biases.
• Nonetheless, so far, existing fairness metrics used in education are predictive performance-oriented, focusing on assessing biased outcomes across groups of students, without considering the behaviors of the models nor the severity of the biases in the outcomes.