AI-fairness and equality of opportunity: a case study on educational achievement

This study focuses on predicting students’ academic performance, examining how AI predictive models often reflect socioeconomic inequalities influenced by factors such as parental socioeconomic status and home environment, which affect the fairness of predictions. We compare three AI models aimed at performing an ablation study to understand how these sensitive features (referred to as circumstances) influence predictions. Our findings reveal biases in predictions that favor advantaged groups, depending on whether the goal is to identify excellence or underperformance. Additionally, a two-stage estimation procedure is proposed in the third model to mitigate the impact of sensitive features on predictions, thereby offering a model that can be considered fair with respect to inequality of opportunity.