Predictive analytics is widely used in various domains, including education, to inform decision-making and improve outcomes. However, many predictive models are proprietary and inaccessible for evaluation or modification by researchers and practitioners, limiting their accountability and ethical design. Moreover, predictive models are often opaque and incomprehensible to the officials who use them, reducing their trust and utility. Furthermore, predictive models may introduce or exacerbate bias and inequity, as they have done in many sectors of society. Therefore, there is a need for transparent, interpretable, and fair predictive models that can be easily adopted and adapted by different stakeholders. In this paper, we propose a fair predictive model based on multivariate adaptive regression splines(MARS) that incorporates fairness measures in the learning process. MARS is a non-parametric regression model that performs feature selection, handles non-linear relationships, generates interpretable decision rules, and derives optimal splitting criteria on the variables. Specifically, we integrate fairness into the knot optimization algorithm and provide theoretical and empirical evidence of how it results in a fair knot placement. We apply our fairMARS model to real-world data and demonstrate its effectiveness in terms of accuracy and equity. Our paper contributes to the advancement of responsible and ethical predictive analytics for social good.

翻译：预测分析广泛应用于包括教育在内的多个领域，以辅助决策并改善结果。然而，许多预测模型具有专有性，研究人员和从业者无法对其进行评估或修改，从而限制了其问责性和道德设计。此外，模型常常不透明且难以被使用它们的官员理解，降低了信任度和实用性。更甚，预测模型可能引入或加剧偏见和不公平，正如在社会诸多领域已发生的情况。因此，迫切需要透明、可解释且公平的预测模型，以便不同利益相关者能够轻松采用和调整。本文提出了一种基于多元自适应回归样条（MARS）的公平预测模型，该模型在学习过程中融入了公平性度量。MARS是一种非参数回归模型，能够执行特征选择、处理非线性关系、生成可解释的决策规则，并推导出变量上的最优分裂标准。具体而言，我们将公平性整合到节点优化算法中，并从理论和实证角度证明其如何实现公平的节点放置。我们将公平MARS模型应用于真实世界数据，并展示了其在准确性和公平性方面的有效性。本文为推动负责任且合乎道德的预测分析助力社会福祉做出了贡献。