Collaborative Research: CIF: Small: Interpretable Fair Machine Learning: Frameworks, Robustness, and Scalable Algorithms

Machine-learning algorithms are revolutionizing modern decision-making processes, from deciding job offers, evaluating loans, and determining university enrollments to proposing medical interventions. However, despite the recent success of machine-learning algorithms in solving large-scale problems, serious concerns have been raised that they are not entirely objective and can inadvertently amplify human biases. The proposed research project addresses this fundamental shortcoming by developing scalable data-driven methods and algorithms that generate interpretable policies aiming for provable fairness guarantees. The project will inform the policy-makers or decision-makers about possible outcomes and tradeoffs between machine learning outcomes and social equity/fairness. Furthermore, the research results will provide guidelines to support policies as well as regulations to promote diversity and fairness in many relevant domains of application. The proposed research leverages recent advances in discrete and robust optimization, aiming for solution methodologies that faithfully address the exact learning models with fairness measures, provide strong out-of-sample fairness guarantees, are robust against bias and noisy outliers in the dataset, and can be solved efficiently for large-scale problem instances. More specifically, the proposed research aims to develop effective new frameworks for fair learning via sub-data selection that can leverage past efforts and enhance the fairness in the learning outcomes. Robust solution schemes will be carefully designed to significantly mitigate the severe overfitting effects of empirical-based methods and improve out-of-sample performance. Efforts will also be devoted to addressing algorithmic fairness in multi-stage decision-making and resource-allocation problems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.