As AI and machine-learned software are used increasingly for making decisions that affect humans, it is imperative that they remain fair and unbiased in their decisions. To complement design-time bias mitigation measures, runtime verification techniques have been introduced recently to monitor the algorithmic fairness of deployed systems. Previous monitoring techniques assume full observability of the states of the (unknown) monitored system. Moreover, they can monitor only fairness properties that are specified as arithmetic expressions over the probabilities of different events. In this work, we extend fairness monitoring to systems modeled as partially observed Markov chains (POMC), and to specifications containing arithmetic expressions over the expected values of numerical functions on event sequences. The only assumptions we make are that the underlying POMC is aperiodic and starts in the stationary distribution, with a bound on its mixing time being known. These assumptions enable us to estimate a given property for the entire distribution of possible executions of the monitored POMC, by observing only a single execution. Our monitors observe a long run of the system and, after each new observation, output updated PAC-estimates of how fair or biased the system is. The monitors are computationally lightweight and, using a prototype implementation, we demonstrate their effectiveness on several real-world examples.

翻译：随着人工智能和机器学习软件越来越多地用于做出影响人类的决策，确保其决策公平且无偏见至关重要。为补充设计阶段的偏差缓解措施，近年来引入了运行时验证技术，用于监测已部署系统的算法公平性。以往的监测技术假设（未知）被监测系统的状态完全可观测。此外，它们只能监测被指定为不同事件概率算术表达式的公平性属性。本研究将公平性监测扩展至建模为部分可观测马尔可夫链（POMC）的系统，以及包含事件序列数值函数期望值算术表达式的规约。我们唯一的假设是底层POMC是非周期的且从平稳分布开始，且其混合时间已知有界。这些假设使我们能够通过仅观测单次执行，估计被监测POMC所有可能执行分布的给定属性。我们的监测器持续观测系统长期运行，并在每次新观测后输出关于系统公平性或偏倚程度的更新PAC估计。该监测器计算开销轻量，通过原型实现，我们在多个真实世界案例中验证了其有效性。