Long-Term Fairness for Real-Time Decision Making: A Constrained Online Optimization Approach

As machine learning (ML)-driven decisions proliferate, particularly in cases involving sensitive attributes, such as gender, race, and age, to name a few, the need for equity and impartiality has emerged as a fundamental concern. In situations demanding real-time decision-making, fairness objectives become more nuanced and complex: instantaneous fairness to ensure equity in every time slot and long-term fairness to ensure fairness over a period of time. There is a growing awareness that real-world systems operating over long periods require fairness over different timelines. Most existing approaches mainly address dynamic costs with time-invariant fairness constraints, often disregarding the challenges posed by time-varying fairness constraints. Time-varying fairness constraints require the learners to adapt their decisions to meet the changing constraints. However, long-term dynamics are hard to assess and accurately predicting the changes in constraints can be difficult. To bridge this gap, this work introduces a framework for ensuring long-term fairness within dynamic decision-making systems characterized by time-varying fairness constraints. We formulate the decision problem with fairness constraints over a period as a constrained online optimization problem. A novel online algorithm, named long-term fairness-aware online learning algorithm (LoTFair), is presented that solves the problem "on the fly." We demonstrate that long-term fairness for real-time decision making can be addressed flexibly and efficiently by LoTFair: it achieves overall fairness while maintaining performance over the long run.