An outstanding issue with machine learning based decision-making algorithms is the inherent trade-offs between different system criteria. There is an emerging body of literature demonstrating trade-offs between fairness and accuracy, and between different fairness notions. By improving fairness, overall accuracy might decrease. Furthermore, different fairness notions are not compatible with each other: well-established results show that common statistical fairness notions are often mutually exclusive. Accurate understanding of such trade-offs is critical for stakeholders and practitioners to appropriately use these machine learning methods. The focus of this project is to take an interdisciplinary approach to study, explain, and address the inherent trade-offs between different system criteria in machine learning-based decision-making.
The researchers will develop methods to capture trade-offs between different system criteria in machine learning algorithms. They will develop visualizations and interactive interfaces to explain the trade-offs between the models to the stakeholders. Finally, the project team will explore social and technical innovations that let stakeholders navigate and negotiate the fundamental trade-offs between different system criteria. The project will disseminate the new knowledge in part through a MOOC offering.
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.
