FAIRPLAI: A Human-in-the-Loop Approach to Fair and Private Machine Learning

The introduction of FAIRPLAI marks a significant advancement in the field of machine learning, particularly as these systems increasingly influence vital decisions in healthcare, finance, and public services. Traditional models often struggle to balance accuracy with fairness and privacy, leading to potential disparities and ethical concerns. FAIRPLAI tackles these issues by incorporating human oversight into the design and deployment of machine learning systems. It constructs privacy-fairness frontiers that clarify the trade-offs between accuracy, privacy guarantees, and group outcomes. Additionally, it allows for interactive stakeholder input, enabling decision-makers to select fairness criteria that align with their specific needs. This innovative approach not only preserves strong privacy protections but also actively reduces fairness disparities, demonstrating its effectiveness in real-world applications. By applying FAIRPLAI to benchmark datasets, researchers can ensure that mach…