A machine-learned system that is fair in static decision-making tasks may have biased societal impacts in the long-run. This may happen when the system interacts with humans and feedback patterns emerge, reinforcing old biases in the system and creating new biases. While existing works try to identify and mitigate long-run biases through smart system design, we introduce techniques for monitoring fairness in real time. Our goal is to build and deploy a monitor that will continuously observe a long sequence of events generated by the system in the wild, and will output, with each event, a verdict on how fair the system is at the current point in time. The advantages of monitoring are two-fold. Firstly, fairness is evaluated at run-time, which is important because unfair behaviors may not be eliminated a priori, at design-time, due to partial knowledge about the system and the environment, as well as uncertainties and dynamic changes in the system and the environment, such as the unpredictability of human behavior. Secondly, monitors are by design oblivious to how the monitored system is constructed, which makes them suitable to be used as trusted third-party fairness watchdogs. They function as computationally lightweight statistical estimators, and their correctness proofs rely on the rigorous analysis of the stochastic process that models the assumptions about the underlying dynamics of the system. We show, both in theory and experiments, how monitors can warn us (1) if a bank's credit policy over time has created an unfair distribution of credit scores among the population, and (2) if a resource allocator's allocation policy over time has made unfair allocations. Our experiments demonstrate that the monitors introduce very low overhead. We believe that runtime monitoring is an important and mathematically rigorous new addition to the fairness toolbox.

翻译：机器学习系统在静态决策任务中表现公平，但长期可能产生有偏的社会影响。当系统与人类交互并形成反馈模式时，可能强化原有偏见并催生新偏见。现有研究尝试通过智能系统设计来识别和缓解长期偏见，而我们则提出实时监控公平性的技术。我们的目标是构建并部署一个监控器，持续观察系统在现实环境中生成的长序列事件，并在每个事件发生时输出当前系统公平性的判定。监控的优势体现在两方面：第一，公平性评估在运行时进行，这至关重要——由于对系统与环境的局部认知、不确定性及动态变化（如人类行为的不可预测性），不公平行为无法在设计阶段先验消除；第二，监控器天生独立于被监控系统的构建方式，适合作为可信的第三方公平性监督者。它们以轻量计算型统计估计器的方式运行，其正确性证明依赖于对刻画系统底层动态假设的随机过程的严谨分析。我们在理论和实验中证明，监控器能够预警：(1) 银行信贷政策随时间推移是否导致群体间信用评分分布失衡；(2) 资源分配器长期分配策略是否造成不公平分配。实验表明监控器引入的开销极低。我们认为运行时监控是公平性工具箱中一项重要且数学严谨的新方法。