Fair Multivariate Adaptive Regression Splines for Ensuring Equity and
  Transparency
Parian Haghighat Denisa G'andara Lulu Kang Hadis Anahideh
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.