Machine learning (ML) has demonstrated remarkable capabilities across many real-world systems, from predictive modeling to intelligent automation. However, the widespread integration of machine learning also makes it necessary to ensure machine learning-driven decision-making systems do not violate ethical principles and values of society in which they operate. As 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 that operate over long periods and require fairness over different timelines. However, existing approaches mainly address dynamic costs with time-invariant fairness constraints, often disregarding the challenges posed by time-varying fairness constraints. 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 LoTFair, is presented that solves the problem 'on the fly'. We prove that LoTFair can make overall fairness violations negligible while maintaining the performance over the long run.

翻译：机器学习（ML）在从预测建模到智能自动化的众多现实系统中已展现出卓越能力。然而，机器学习的广泛集成也使得确保由机器学习驱动的决策系统不违反其所处社会的伦理原则与价值观成为必要。随着以机器学习为驱动的决策日益普及，特别是在涉及性别、种族、年龄等敏感属性的场景中，对公平性与公正性的需求已成为一个根本性问题。在需要实时决策的情境下，公平目标变得更为细致与复杂：即时公平性确保每个时段的公平，而长期公平性则确保一段时间内的公平。人们日益认识到，长期运行且需在不同时间尺度上满足公平性的现实系统，其现有方法主要处理具有时不变公平约束的动态成本，往往忽视了时变公平约束带来的挑战。为弥补这一不足，本文提出一个框架，用于在具有时变公平约束的动态决策系统中确保长期公平。我们将带有时段公平约束的决策问题形式化为一个带约束的在线优化问题，并创新性地提出一种名为LoTFair的在线算法，以“即时”方式求解该问题。我们证明，LoTFair能够在长期保持性能的同时，使整体公平违规程度可忽略不计。